MatLab versión 6 - Facultade de Informática

Transcripción

MatLab versión 6
FACULTADE DE INFORMÁTICA
SOFTWARE
Matlab Versión 6
Introducción
En los últimos años la Facultad de Informática de A Coruña ha venido utilizando el software Matlab para la realización de los
créditos prácticos en las tres titulaciones impartidas en la Facultad: Ingeniería Informática, Ingeniería Técnica Informática de
Gestión e Ingeniería Técnica Informática de Sistemas.
Matlab, desarrollado por la compañía MathWorks, es un producto ampliamente reconocido para la informática técnica que abarca
gran variedad de áreas de aplicación, entre las que se encuentran el procesamiento de imágenes, sistemas de mando, ciencias
naturales, finanzas, economía...
Este entorno incluye herramientas para adquisición y análisis de datos, visualización y procesamiento de las imágenes, prototipos
de algoritmos, planificación y simulación, programación y desarrollo de aplicaciones y un largo etcétera. Dentro de un entorno
muy amigable con una facilidad extraordinaria para la resolución de problemas relacionados con diversas disciplinas.
Matlab integra informática matemática, visualización y un lenguaje técnico muy poderoso. Permitiendo además importar rutinas
externas escritas en LENGUAJE C, C++, Fortran y Java a sus aplicaciones.
Existen diversas implementaciones Open Source compatibles en cierta medida con Matlab, como Octave y Scilab que dan una
muestra de como se puede aprovechar el "saber hacer" de MathWork, puesto que incorporan sus técnicas de trabajo y sus
entornos amigables.
Hasta ahora se han utilizado los módulos de Matlab, Simulink, Neural Network Toolbox, Signal Processing Toolbox, Wavelet
Toolbox y Communications Blockset en asignaturas tales como Sistemas Conexionistas, Tratamiento Digital de la Señal,
Técnicas de Simulación, etc.
http://www.fi.udc.es/cc/srv/sft/Matlab6.html (1 of 3) [25/10/2004 11:41:42]
MatLab versión 6
Matlab Versión 6
En Marzo de 2001 se ha instalado para uso docente en la Facultad de Informática la versión 6 de Matlab que proporciona un
entorno mejorado, cuyas características más reseñables son:
● Nuevo escritorio con una carpeta de herramientas para manejar el entorno de MATLAB, incluida la Ventana de Orden, la
ventana de Historia de Orden, Workspace Browser, Editor de Serie, etc.
● Mejora del cómputo matemático y perfeccionamiento de los algoritmos: la biblioteca de LAPACK esta perfeccionada para
realizar cálculos de matrices más rápidos, nuevos solvers de ecuaciones diferenciales y algoritmos de cuadratura más
exactos.
● Nuevas estadísticas de los datos y las herramientas básicas para el análisis rápido de datos trazados.
● Nuevos rasgos avanzados de visualización: despliegue de imágenes 2-D, superficies y volúmenes como objetos
transparentes; la barra de herramientas de la cámara interactiva para controlar perspectivas.
● Nueva interface para llamar rutinas de Java y los prebuilt usando Java objet directamente de MATLAB.
● Nueva interface de comunicación del puerto serie para comunicar con instrumentos externos a MATLAB.
● Integración de MATLAB con Microsoft Visual Estudio.
● Ventana de ayuda que permite buscar, leer e imprimir la documentación en linea.
Algunos Toolboxes utilizados en la docencia de la Facultad de Informática de A Coruña han sido mejorados, tales como:
● Signal Processing Toolbox 5: proporciona herramientas para el análisis de señales y sistemas lineales, para modelar datos
de series temporales y para el desarrollo de algoritmos.
● Filter Design Toolbox 2: complementa al Signal Processing Toolbox 5 proporcionando técnicas de diseño de filtros
avanzados.
● Control System Toolbox 5: proporciona un entorno interactivo para modelar, analizar y diseñar sistemas de control.
● Neural Network Toolbox 4: proporciona los algoritmos mas utilizados en el diseño y simulación de redes de neuronas.
Uso
MatLab versión 6
Para la ejecución de este entorno de trabajo en los sistemas de docencia deberá realizar su conexión a la maquina de alias matlab.
El programa se ejecutará utilizando la instrucción matlab.
Información adicional
Para una mayor información sobre las mejoras introducidas en esta nueva versión de Matlab se pueden consultar las siguientes
páginas:
● Características generales de la release 12: [HTML]
● Nuevas características de Matlab 6: [HTML] [PDF]
● Nuevas características de Simulink 4: [HTML] [PDF]
● Nuevas características de Control System Toolbox 5: [HTML] [PDF]
● Nuevas características de Neural Network Toolbox 4: [HTML] [PDF]
● Nuevas características de Signal Processing Toolbox 5: [HTML] [PDF]
● Nuevas características de Wavelet Toolbox 2: [HTML] [PDF]
● Nuevas características de Communications Blockset 2: [HTML] [PDF]
© Copyright 2001. Centro de Cálculo da Facultade de Informática.
What’s New for M ATLAB 6?
®
Feature highlights for MATLAB 6 and other new and updated Release 12 products
for analysis, visualization, algorithm development, and application deployment.*
NEW AND UPDATED PRODUCTS
DATA ACQUISITION AND ACCESS
NEW!
Instrument Control Toolbox
5
Data Acquisition Toolbox
6
Database Toolbox
6
DATA ANALYSIS AND MATH
Signal Processing Toolbox
The new MATLAB 6 desktop
NEW!
7
Filter Design Toolbox
7
Control System Toolbox
8
Neural Network Toolbox
8
Statistics Toolbox
9
Spline Toolbox
9
APPLICATION DEPLOYMENT
Data acquisition with the new Instrument
Control Toolbox
MATLAB Compiler
10
MATLAB C/C++ Math Library
10
MATLAB C/C++ Graphics Library
10
MATLAB Runtime Server
11
MATLAB Web Server
11
FINANCE AND ECONOMICS
*Also included in Release 12 are numerous other products,
including tools for image processing, control design, and
DSP and communications. For information on these additional
products, go to www.mathworks.com
MathWorks customers who are under subscription service will
receive their new Release 12 CD automatically.
The MathWorks
NEW!
Datafeed Toolbox
12
Financial Derivatives Toolbox
12
M ATLAB 6
for computation, analysis, visualization, and development
MATLAB 6 makes learning and using MATLAB easier, more efficient, and more effective. The new desktop
front end offers a portfolio of tools that work together to transform MATLAB into an even more intuitive,
integrated environment for exploration and development. These tools provide quick access to MATLAB
code, variables, data files, graphics, and online help, enabling you to find what you need when you need it.
New point-and-click tools make common importing and plotting tasks easy and give you fast
access to the features you need to produce presentation-quality graphics. You can modify any element of
your plots without coding, eliminating the need to remember command names and graphical attributes.
MATLAB 6 also incorporates the LAPACK and FFTW libraries, several new mathematical algorithms, and
new interfaces to external data and to Java.
NEW FEATURE HIGHLIGHTS
MATLAB Desktop
■
■
■
■
■
■
■
2
New desktop interface with all the tools
for managing your MATLAB environment
Command Window for interactively
exploring MATLAB
Command History tool for logging executed
MATLAB commands and preserving them
across sessions for reference and reuse
Help/Help Navigator windows for accessing, reading, and searching the online
HTML documentation (browser displays
documentation for all installed products
concurrently)
Workspace Browser for viewing and
modifying variable values, types, and
hierarchy (integrated with Array Editor
and Import Wizard)
Array Editor for viewing and editing
array contents in a tabular format
(linked with the Workspace Browser
for easy flow between variable
viewing and editing)
Launch Pad for accessing documentation,
demos, and GUI-based tools from all
installed MathWorks products
The new MATLAB desktop configured with the Command Window and the
Launch Pad, Workspace Browser, Command History, and Array Editor tools.
The data visible in the Array Editor corresponds to the highlighted variable
in the Workspace Browser window.
■
■
Current Directory browser for viewing,
maintaining, and searching MATLAB files
Enhanced M-file Editor/Debugger
for creating, editing, and debugging
MATLAB M-files
The Help window (above) displays a
list of topics in the Help Navigator
(left portion of window) found when
the user initiated a search on
“transparency.” The relevant help
section (right portion of window)
includes a 3-D graphic and the code
that created it.
Language and GUI Development Features
■
■
and feel, and provides more powerful
tools for layout and alignment, property
viewing and setting, and menu editing.
When a new GUI is saved, MATLAB
captures the results in an M-file in a
structured outline framework that can
serve as the basis for developing an
entire application.
MATLAB Java interface for
calling Java code directly from
MATLAB includes support for creating
and using Java objects, allowing you to
use Java libraries directly from MATLAB.
GUIDE, a MATLAB tool for GUI
prototyping and development, is now
more responsive, has an intuitive look
■
■
Function handle data type stores
function information for faster
function evaluation with feval.
New MATLAB add-in for Visual Studio
lets you create MATLAB functions
(MEX-files) from C and C++ code in
the Microsoft® Visual Studio environment.
Advanced Visualization Features
■
■
Display of 2-D images, surfaces,
and volumes as transparent objects,
enabling visualization of obscured
layers of overlapping data and
interior views of closed volumes
Camera toolbar and menu options
to control the point of view (orbit,
tilt, vertical, horizontal); principal axis
(scene orientation); scene light; and
projection type
■
■
OpenGL rendering, offering significant
increases in display speed with graphics
hardware
Improved performance for large data
sets and animations
View of a lit 3-D isosurface with endcaps using the new
smooth3 function to smooth the data. The new
camera control toolbar is visible below the plot toolbar.
Plot Editing, Annotation, and Analysis Features
■
■
■
■
New plotting toolbar and menus for
easily creating, editing, and annotating
plot titles, legends, axis labels, freeform
text, arrows, and lines
Functions for overlaying plots of basic
data statistics for x and y, such as
minimum, maximum, and mean
New graphical Property Editor for viewing
and modifying any plot or GUI attribute
Windows-style page setup for improved
hardcopy output
■
■
Basic Fitting GUI for computing polynomial fits and spline interpolation, with
optional display of one or more fits
on the plot
Additional Basic Fitting options,
including:
• Saving the fit to the workspace
• Computing and displaying the
fit residuals
• Evaluating a fit (by interpolation
or extrapolation) and producing
a table or plot of the results
• Annotating a plot with fit
equations and residuals
This 2-D MATLAB graphic tracks CO2
concentration over time (measured in
years). Using the Basic Fitting menu
option in the new graphics toolbar Tools
menu and the Basic Fitting dialog (right),
4th and 7th degree polynomial fits have
been interactively applied to the data.
Data compiled by J.M. Barnola et al.,
Worldwatch Institute.
3
Data Access and File Format Enhancements
■
■
■
■
■
■
■
■
New Import Wizard for easy importing of external
data from a file into the MATLAB workspace
Serial port interface for direct access to peripheral
devices such as modems, printers, and scientific
instruments via your computer’s RS-232, RS-485,
and RS-422 ports (PC, Solaris, Linux)
Importing of Microsoft Excel worksheets
Improved figure export to other programs, such as
Microsoft Word and PowerPoint (Clipboard via EMF files)
Importing and exporting of Windows AVI files
(uncompressed only for UNIX)
Importing of GIF format data files
Importing of Windows Icon and Cursor files
(all platforms)
CCITT Group 3 and 4 fax support for TIFF files
The Import Wizard allows you to preview the contents of data files before importing them. A preview
of the list of variables is displayed in the membrane.mat file (left). The data in the variable L
(highlighted) is displayed in the window on the right.
Mathematical Computation and Algorithm Enhancements
■
■
■
■
■
4
Matrix math in MATLAB now incorporates the well-known LAPACK library
(which replaces LINPACK and EISPACK),
increasing MATLAB’s speed on computations involving matrices of order several
hundred.
FFT functions such as fft and ifft
now rely on the MIT FFTW library,
resulting in speedups particularly for
composite, prime, and large prime
factor array lengths.
New functions let you write C MEX-files
that call LAPACK and BLAS directly
from MATLAB.
Qhull-based functions extend Delaunay
functionality to three or more dimensions.
New griddatan and griddata3
functions enable data gridding and
hyper-surface fitting for three or
more dimensions.
■
■
■
■
New differential equation solvers
solve two-point boundary value
problems for ODEs by collocation
initial-boundary value problems
for parabolic-elliptic PDEs.
The eigs and svds sparse matrix
computation functions now use the
well-known Fortran ARPACK library.
The symmlq, minres, and lsqr
sparse matrix computation functions
now solve symmetric indefinite and
rectangular systems.
Faster, more accurate quadrature
algorithms now handle singularities.
PRODUCTS FOR DATA ACQUISITION AND ACCESS
Instrument Control Toolbox 1
The Instrument Control Toolbox allows you to communicate with data acquisition devices and instruments,
such as spectrum analyzers, oscilloscopes, and function generators. Support is provided for GPIB and VISA
communication protocols. The toolbox lets you generate data in MATLAB to send out to an instrument or read
data into MATLAB for analysis and visualization.
■
■
■
■
■
■
■
Support for:
• GPIB (IEEE-488, HPIB) interfaces
from Agilent, CEC, ComputerBoards,
IOTech, Keithley, and National
Instruments
• VISA (Serial, GPIB, VXI, GPIB-VXI)
interfaces from Agilent and National
Instruments
Graphical user interfaces for instrument
setup and communication
Reading and writing of binary and
ASCII data
Event handling for errors, timeout, bytes
available, data written, and other events
Recording of data transferred to and
from instruments
Synchronous and asynchronous
(blocking and non-blocking) read
and write
Advanced serial port (RS-232, RS-422,
RS-485) support
The Instrument Control Toolbox provides tools for communicating with instruments
and external devices. Two GUIs, instrcreate and instrcomm , allow you
to easily configure and control your instruments.
5
Data Acquisition Toolbox 2
The Data Acquisition Toolbox allows you to control and communicate with a variety of data acquisition hardware
devices. You can configure external hardware, stream live data directly into MATLAB for analysis, and send data
out. Version 2 of the Data Acquisition Toolbox now includes support for ComputerBoards hardware.
■
■
Support for ComputerBoards devices, including
hardware that supports analog input, analog
output, and digital I/O
Data Acquisition Adaptor Kit for developing
custom interfaces to data acquisition devices
Data read into MATLAB can
be analyzed and visualized
while it is being acquired.
Here, a signal from an
oscilloscope is visualized in
a MATLAB graphics window.
Database Toolbox 2.1
The Database Toolbox allows you to query and exchange data with most ODBC/JDBC databases directly
from MATLAB. Version 2.1 of the Database Toolbox makes it easier to query data and quickly access the
MATLAB report-generation tools. This version performs approximately 100 times faster than version 2.
The Database Toolbox is now available on all platforms for which MATLAB offers Java support.
■
■
Group qualifier in the Where Clause
dialog makes it easier to interactively
select multiple query criteria.
Report Generator function enables access
to the MATLAB Report Generator directly
from within the Visual Query Builder.
The new Group option in the Where Clause dialog makes it easier to graphically select multiple query criteria.
6
PRODUCTS FOR DATA ANALYSIS AND MATH
Signal Processing Toolbox 5
The Signal Processing Toolbox provides tools for signal and linear system analysis, time series data
modeling, and algorithm development. Version 5 of the Signal Processing Toolbox provides a new
graphical user interface for designing and analyzing digital filters. This interface builds on SPTool,
the toolbox’s original GUI, which contains support for signal viewing and spectral analysis.
■
■
■
New GUI-driven Filter Design and
Analysis Tool provides functions for
interactively designing, evaluating,
and comparing filters.
SPTool can now play a selected portion
of a signal to a sound card, eliminating
the need to create subsets of the signal
before listening.
SPTool offers signal markers with
improved readouts that you can easily
manipulate.
■
FIR filter enhancements include automatic order adjustment and improvements to remez.
FDATool lets you create and edit lowpass,
highpass, bandpass, and bandstop FIR
and IIR digital filters. You can graphically
design filters from scratch or import
previously designed filters.
Filter Design Toolbox 2
The Filter Design Toolbox complements the Signal Processing Toolbox by providing advanced filter design
techniques for designing, simulating, and analyzing fixed- and floating-point filters. The functions in the
Filter Design Toolbox can help you determine the effects of fixed-point processing. You can then evaluate
a trade-off between required precision width and available design resources.
■
■
■
Provides advanced techniques for designing,
simulating, and analyzing digital filters
Simplifies the design of fixed-point filters and
analysis of quantization effects
Extends the capabilities of the Signal Processing
Toolbox, adding architectures and design methods
for complex real-time DSP applications
State trajectories showing
normal behavior and overflow
limit-cycles.
7
Control System Toolbox 5
The Control System Toolbox provides an interactive environment for modeling, analyzing, and designing control
systems. Version 5 of the Control System Toolbox introduces the SISO Design Tool, a new GUI that displays root
locus and Bode diagrams. You can use both root locus and frequency design techniques to perform rapid
iterations on compensator design for SISO systems. The enhanced LTI Viewer is now dynamically linked to the
SISO Design Tool. These two GUIs let you design compensators graphically without using the command line.
■
■
■
New SISO Design Tool for compensator
design
Enhanced LTI Viewer with data markers,
better grids, and sharper plots
Tools for setting preferences and
customizing plots
■
■
Algorithmic enhancements, including
stability margins and sharper root
locus plots
Comprehensive new Getting Started
manual
The SISO Design Tool simplifies the task
of designing controllers. You can click-anddrag to move the compensator poles and
zeros and then automatically update the
open- and closed-loop response plots.
Neural Network Toolbox 4
The Neural Network Toolbox provides generalized versions of the most commonly used network paradigms
and algorithms for neural network design and simulation. Version 4 of the Neural Network Toolbox adds an
intuitive and comprehensive GUI for designing and managing neural networks. This version also contains a
suite of new features, examples, and Simulink blocks to help you use the toolbox in control system applications.
■
■
GUI tools for:
• Entering, importing, and
exporting data
• Creating, initializing, training,
and simulating networks
• Visualizing and evaluating
network performance
New control system examples that
illustrate model predictive control, feedback linearization, and model reference
adaptive control
■
■
Performance benchmarks for selecting the
appropriate algorithm for your application
Functions that apply weight and bias
learning rules in both batch and incremental mode
The Neural Network
user interface allows you
to import and export data,
as well as create, view, train,
and simulate networks.
8
Statistics Toolbox 3
The Statistics Toolbox includes functions and interactive tools for analyzing historical data, modeling and
simulating systems, developing statistical algorithms, and learning and teaching statistics. Version 3 of the
Statistics Toolbox enhances support for linear models, with new functions for the analysis of variance and
support for additional regression techniques. Also included is new support for nonnormal data using robust
techniques, generalized linear models for a variety of nonnormal distributions, and nonparametric techniques.
■
■
■
■
■
■
New linear model functions, including:
• Multivariate ANOVA, with graphics
functions for examining data
• Multi-way and nonparametric ANOVA
• Analysis of covariance (ANOCOVA)
Multiple comparisons of means and
other estimates
Multiple response surface fitting
Calculation of simultaneous
confidence bounds
Support for robust regression and
generalized linear models
Capability for importing numeric
and text data from tab-delimited files
■
■
■
Additional functions for distribution
testing and plotting
New fractional factorial design generation
Multivariate t random number generation
The gplotmatrix function creates a matrix
of scatter plots. In this example four measures
of automobile performance are compared: miles
per gallon (MPG), acceleration, weight, and
horsepower. You can also use the function to
group variables. In this example, the year of
manufacture has been added to the analysis.
Spline Toolbox 3
The Spline Toolbox provides powerful features for data fitting and visualization and for interpolation and
extrapolation, allowing you to fit a curve or surface through a set of observational data. Version 3 of the
Spline Toolbox gives you easy access to this functionality through a new GUI, as well as features that help
you learn about and work with splines more easily and productively.
■
New graphical user interface allows
you to:
• Create and manage various spline
approximations
• Add, delete, and move data or knots
• Vary parameters that affect a spline fit
• View the first or second derivative of
a spline or its error
• Save splines to the workspace
• Observe the underlying toolbox
commands that generate the spline
■
■
New command-line option that lets
the approximation functions determine
the knots
Enhanced documentation with an
expanded tutorial and a new glossary
With the new Spline Tool
you can view and
compare multiple spline
fits on the same plot.
9
PRODUCTS FOR APPLICATION DEPLOYMENT
MATLAB Compiler and C/C++ Libraries
The MATLAB Compiler Suite, which consists of the MATLAB Compiler and the MATLAB C/C++ Math and
Graphics Libraries, allows you to convert your MATLAB M-files automatically to C and C++ for use as
stand-alone programs. Version 2.1 of the MATLAB Compiler Suite contains new optimizations that
improve executable speed and significantly increase the range of compilable applications. A new
component, the MATLAB add-in for Visual Studio development system, lets you use the Compiler from
within Microsoft Visual Studio.
■
■
■
■
■
■
■
Improved optimization of generated
C/C++ code
Support for the inclusion of MEX-files in
stand-alone applications
Support for the compilation of
M-files containing:
• input calls with no workspace
variable arguments
• load/save
• eval calls with no workspace
variable arguments
• pause
• Function handles (New MATLAB feature)
New MLIB file capability for packaging
multiple compiled files as a single,
shared library
The MATLAB add-in for Visual Studio,
a new feature in MATLAB 6, allowing
you to compile, edit, and run M-files
from Microsoft Visual Studio using
the Compiler Suite
Folding for scalar and non-scalar
valued array constants
New support for integer data types
(int 8, 16, 32 and uint 8, 16, 32),
enabling the conversion of image processing applications to C/C++ code
Using the MATLAB add-in
for Visual Studio you can
compile, edit, and run M-files
within the Microsoft Visual
C/C++ environment.
The Compiler Suite lets you automatically convert many MATLAB
applications, like this windplot example, to C and C++.
10
MATLAB Runtime Server 6
The MATLAB Runtime Server allows you to take an existing MATLAB application and turn it into a standalone product that is easy and economical to package and distribute. Version 6 of the MATLAB Runtime
Server supports the latest MATLAB 6 functionality. Additional product and documentation enhancements
make it easier to develop Runtime Server applications.
■
■
■
New buildp function generates runtime
p-code for applications automatically.
depfun now locates Java class-dependent functions.
Improved documentation provides more
detailed examples and templates, more
comprehensive descriptions of product
features, and additional information on
application setup.
The Runtime Server lets you
convert any MATLAB application
to a stand-alone deployable
application. This Query by
Singing example queries
360 songs through pattern
recognition. The user triggers
the song-retrieval process by
singing part of the song.
MATLAB Web Server 1.2
The MATLAB Web Server allows you to embed MATLAB math and graphics in your Web applications. Version 1.2
of the MATLAB Web Server contains enhancements that improve performance for NT servers, increase
security, and support the Linux platform.
■
■
■
The NT Web Server architecture now
keeps MATLAB sessions open once
started, significantly reducing startup time.
System administrators can now limit
user access (particularly beneficial when
the Web Server application is operating
outside a firewall or directly on the HTTP
server machine).
The Web Server can now run on a
system whose architecture differs from
that of the HTTP server machine.
The aeronautical, financial, and
mechanical applications shown here
are among the many Web-deployable
applications that you can develop with
the MATLAB Web Server.
11
PRODUCTS FOR FINANCE AND ECONOMICS
Datafeed Toolbox 1.2
The Datafeed Toolbox lets you download a wide variety of security data from financial data servers into
your MATLAB workspace. You can then pass this data to MATLAB or to another toolbox, such as the
Financial Time Series Toolbox, for further analysis. Version 1.2 of the Datafeed Toolbox provides support
for two financial data servers in addition to Bloomberg.
■
■
■
Access to data from Interactive
Data’s Remote Plus
Access to data from Yahoo!’s
financial Web site, quote.yahoo.com
IDC and Yahoo! support incorporated
into the Datafeed Toolbox graphical
user interface
In this example, the Datafeed Toolbox
interface is used to make a Bloomberg
connection and then request and chart
data for a stock price.
Financial Derivatives Toolbox 1
The Financial Derivatives Toolbox, an extension to the Financial Toolbox, allows you to create and manage
portfolios containing several types of financial instruments and calculate their prices and sensitivities. It also
offers functionality for assessing the fundamental hedging tradeoffs.
■
■
■
Create and manage the following instrument
portfolios:
• Bonds and options on bonds
• Fixed rate and floating rate notes
• Caps and floors
• Vanilla swaps
Calculate prices and sensitivities based on the
Heath-Jarrow-Morton (HJM) model or on a
given interest rate term structure
Perform hedging analysis
The MathWorks
Tel: 508.647.7000 [email protected] www.mathworks.com
The treeviewer function
displays a diagram of the HJM tree,
allowing you to interactively examine
the values on the nodes of the tree.
This example shows the prices of a
4% bond along the top and bottom
branch paths of the HJM price tree.
9858v01 10/00
© 2000 by The MathWorks, Inc. MATLAB, Simulink, Stateflow, Handle Graphics, and Real-Time Workshop are registered trademarks, and Target Language Compiler is a trademark of The MathWorks, Inc. Other product or brand names are trademarks or registered trademarks of their respective holders.
Simulink 4
®
for modeling, simulation, and analysis of dynamic systems
Simulink® is an interactive tool for modeling,
simulating, and analyzing dynamic systems.
It enables you to build graphical block
diagrams, simulate dynamic systems,
evaluate system performance, and refine
your designs. Simulink integrates seamlessly
with MATLAB®, providing you with immediate access to an extensive range of analysis
and design tools. These benefits make
Simulink the tool of choice for control system
design, DSP design, communications system
design, and other simulation applications.
KEY FEATURES
USABILITY
■
■
■
■
■
Extensive library of predefined blocks
Graphical debugger
Model Browser for navigating model hierarchies
Finder for searching models and libraries
Customizable blocks that can incorporate existing
C, Ada, MATLAB, and Fortran code
COMPUTATIONAL SUPPORT
Creating Models
■ Linear, nonlinear, continuous-time, discrete-time,
multirate, conditionally executed, mixed-signal,
and hybrid systems
■ Support for matrix signals and operations
■ Bitwise Logical Operator block logically masks, inverts,
or shifts the bits of an unsigned integer signal
Simulink provides a complete set of modeling tools that you can use to quickly develop
detailed block diagrams of your systems.
Features such as block libraries, hierarchical
modeling, signal labeling, and subsystem
customization provide a powerful set of
capabilities for creating, modifying, and
maintaining block diagrams. These modeling
features, together with Simulink’s comprehensive set of predefined blocks, make it
easy to create concise representations of
your systems, regardless of their complexity.
An engine model uses Trigger blocks to
model conditionally executed behavior.
As a function of the crankshaft angle, a
pulse triggers a cylinder to fire.
1
Desired
rpm
pi/30
rpm
to
rad/s
Kp
Proportional Gain
Ki
T
z-1
Integral Gain
0
2
N
limit
output
1
1
Throttle Ang.
mass(k+1)
Discrete-Time
Integrator
controller output
prevent windup
1
Init
Unit Delay
1
z
mass(k)
2
integrator input
enable integration
[0.152]
Trigger
trigger
KEY FEATURES
(continued)
LARGE MODEL DEVELOPMENT
■ Models can be grouped into hierarchies to create a
simplified view of components or subsystems
■ Simulink data objects enable you to create applicationspecific MATLAB data types for your Simulink models
■ Simulink Explorer GUI for viewing and editing data
objects
■ Library Browser for convenient block selection
■ Intellectual property protection using S-functions
(requires Real-Time Workshop® 4.0)
■ Simulations can be run from the MATLAB command line,
either interactively or in batch mode
Extensible Block Library
The Library Browser makes
Simulink comes with more than 200 built-in
blocks that implement commonly required
modeling functions. The blocks are grouped
into libraries according to their behavior:
Sources, Sinks, Discrete, Continuous,
Nonlinear, Math, Functions & Tables, and
Signals & Systems.
it easy to navigate through
In addition, Simulink offers features for
creating customized blocks and block
libraries. You can customize not only the
functionality of a block, but also its user
interface, using icons and dialog boxes. For
example, you can create blocks to model the
behavior of specialized mechanical, circuit,
or software components, such as motors,
inverters, servo-valves, power plants, filters,
tires, modems, receivers, or other dynamic
components. Custom blocks can be saved
in your own block library for future use
and can be shared with work groups,
vendors, and customers.
block libraries and then drag
and drop selected blocks
onto your model.
S-Functions
An S-function (system-function) is a custom
code module that defines the behavior of a
Simulink block. Simulink provides templates for creating your own S-functions
using existing or newly-developed code
(C, Ada, Fortran, or MATLAB). Once you
have created an S-function, you can
include it in your model, using Simulink’s
S-function block.
S-functions reduce the time required to
model large-scale systems by allowing you
to incorporate existing code into your
model. Simulink provides multi-port and
multi-rate S-function support to enhance
usability and permit different sample times
(C and MATLAB only).
Masks
Simulink’s mask editor allows you to create a
custom user interface, called a mask, for any
subsystem or S-function block. The mask
can include a custom icon, parameter dialog,
online help, and initialization script. Custom
masks allow you to tailor a block’s appearance
and user interface for specific applications.
This feature makes it easy to reuse blocks
across multiple systems, as well as systems with
large numbers of models, and models with
many levels. You can modify a block’s behavior
and its attributes in every model simply by
applying the change to the library source.
Configurable Subsystem Block
A Configurable Subsystem block represents
any block contained in a specified library of
blocks. Using the Configurable Subsystem
block’s dialog box, you can specify which
block in the library it represents. You can
also specify the inputs and outputs of the
selected block.
The Simulink Explorer provides you with a graphical user interface for viewing and editing Simulink data objects.
Using the Simulink Explorer, you can view most classes of variables in the MATLAB workspace, and filter and sort
Configurable Subsystem blocks simplify the
creation of models that represent families
of designs. For example, suppose that you
want to model an automobile that offers a
choice of engines. To model such a design,
you would first create a library of models of
the engine types available with the car. You
would then use a Configurable Subsystem
block in your car model to represent the
choice of engines. To model a particular
variant of the basic car design, you need
only choose the engine type, using the
configurable engine block’s dialog. This
enables you to rapidly swap design choices
in and out of your model.
variables by variable name and class. You can also view and edit property values.
Simulink Data Objects
Simulink is used in many applications to
create high-fidelity plant models of realworld systems and to design algorithms to
control these systems. To represent these
systems and algorithms more accurately,
you can use Simulink data objects to define
new MATLAB data types that are specific
to your application and then use them as
parameters and signals in your Simulink
models. You can view and edit all Simulink
data with the Simulink Explorer.
Model Library Support
Model library support makes it easy to build
and maintain libraries of customized blocks.
You can create a block whose properties are
defined in the model library. Then, when you
make a change to the library version of the
block, the change propagates through any
models that use that block.
Short-Time
Spectrum
DF2T
The short-time fast Fourier transform (FFT) block is a masked subsystem in this model,
built using the DSP Blockset. The parameters for the short-time FFT block are controlled
through the dialog box (top right image). The block diagram for the detailed subsystem
(center image) remains hidden from view until the user chooses to reveal it.
hamming Out
1
In
|FFT| ^ 2
In
Win
Window
Magnitude
FFT
In
Direct-Form II
Transpose Filter
Out
Normalization
1
Out
Model Navigation Tools
Library Browser (Windows only)—provides
a tree-structured view of all block libraries
installed on your system.
Model Browser (Windows only)—enables
you to navigate your model hierarchically,
and open systems directly in your model.
Finder dialog box—enables you to search
Simulink models for objects that satisfy
specified search criteria.
The Mux block is used to collect multiple
signals into a vectored signal bundle that can
function as a data bus. The Demux block is
used to disassemble vectored signals so that
they can be accessed as individual signals.
The Bus Selector block provides support for
larger models by making it easy to select a
subset of signals from a bus defined by a
Mux or another Bus Selector block.
Block diagram zooming—greatly simplifies
model viewing by allowing you to enlarge or
shrink the view.
Because most Simulink blocks support
vectored operations, you can greatly reduce
the number of blocks needed to model your
system. This results in clean, simple, and
easy-to-read block diagrams.
Scalar and Vector Connections
Matrix Signal Support
Simulink supports the modeling of singleinput/single-output (SISO) and multi-input/
multi-output (MIMO) systems.
Many Simulink blocks accept or output
matrix signals. A matrix signal is a twodimensional array of signal elements
represented by a matrix. Each matrix
element represents the value of the corresponding signal element at the current time
step. You can use Simulink source blocks
(for example, Sine Wave or Constant) to
generate matrix signals.
You can use the following Simulink blocks
for matrix operations on matrix signals:
• The Product block supports both elementby-element and matrix multiplication and
inversion of inputs.
• The Gain block supports matrix and
element-by-element multiplication of the
input signal by a gain factor. Both input
signals and gain factors can be matrices.
You can use Simulink’s Mux and Demux
blocks to multiplex matrix signals. For
example, you can:
• Generate signal buses by feeding matrix
signals into Mux blocks along with vector
or scalar signals
• Manipulate the elements of a signal bus
by splitting it into its components using
a Demux block, and then connecting the
demuxed signals to nonvirtual blocks,
such as the Gain block
1-exp(-deltat1/Ts)
z-exp(-deltat1/Ts)
Stick Filter
Ka
z-exp(-deltat1/Tal)
Zero-Order
Hold
Alpha Sensor Filter
Kf
1
1-exp(-deltat1/Tal)
1
States
Zero-Order
Hold
T
Act.
Comd.
Ki
z-1
Zero-Order
Hold
Discrete-Time
Integrator
Actuator Pos.
ActPos
Kq
-K-
1-exp(-W2*deltat1)
z-exp(-W2*deltat1)
Pitch Sensor Filter
This Simulink model represents a digital control system for
an aircraft. The Simulink debugger allows you to graphically
diagnose modeling errors. The debugger lets you step
through the simulation block by block, or run to a breakpoint. The currently executing block is displayed in yellow.
You can also display block states, block inputs and outputs,
and other information while running a model.
Zero-Order
Hold
StopInt
Error
Anti-Wind-Up
2
Template
Act. Pos.
Cmd.
Act. Meas.
Configurable Actuator
Act. Pos.
Cmd. [Linear]
Act. Meas.
To create a configurable subsystem, you first create a library of blocks representing the various
block configurations. Then, within a model, you can choose a block from your library using the
configurable subsystem's right-click menu.
Linear Actuator Subsystem
Act. Pos.
Cmd. [Non-Linear]
Act. Meas.
Non-Linear Actuator Subsystem
Simulink debugger has both graphical
and command-line user interfaces.
State-of-the-Art Integration Algorithms
Simulation
After building your block diagram in
Simulink, you can debug it using the interactive Simulink debugger. Then, you can run
interactive simulations and view the results
live. The powerful suite of solvers available
in Simulink make simulation results
extremely accurate.
Simulink Debugger
The Simulink debugger is an interactive
tool for locating and diagnosing errors
in a Simulink model. It enables you to
quickly pinpoint problems in your model
by running simulations step-by-step
and displaying intermediate block states
and input and output values. The
The Simulink simulation engine offers
numerous features for simulating large,
challenging systems. Foremost among
these is the set of integration algorithms,
called solvers, that are based on the MATLAB
ordinary differential equation (ODE) suite.
These solvers are well suited to continuoustime (analog), discrete-time, hybrid, and
mixed-signal simulations of any size. In
addition, they provide fast, reliable, and
extremely accurate simulation results. For
complete handling of discrete systems, the
DSP Blockset is also recommended.
The solvers support differential algebraic
equations (DAEs) with multichannel algebraic loops. An algebraic constraint block
facilitates the solution of a system in which
an algebraic constraint applies to the governing set of equations. The solvers also support
stiff systems, systems with algebraic loops,
and systems with state events (such as
discontinuities, including instantaneous
changes in plant dynamics).
Conditionally Executed Subsystems
With Simulink, you can build and simulate
models with subsystems that execute
conditionally; and are therefore dependent
upon controlling logic signals. The signals
can either enable or trigger the execution
of the subsystem.
Two blocks, the Trigger block and the
Enable block, can be placed in any Simulink
subsystem. An enabled or triggered subsystem includes an additional input signal to
handle controlling logic.
When conditionally executed subsystems are
disabled they are not executed during the
simulation, which noticeably improves processing speed within multimode systems.
Event-Based Simulation Support
Simulink is tightly integrated with Stateflow®,
the MathWorks’ solution for modeling event-
driven behavior. The seamless interaction
between Simulink and Stateflow gives you the
ability to model and simulate your system’s
dynamic and event-driven behavior as a
single, integrated system. (For example,
Simulink and Stateflow share an integrated
Finder.) Designers of automotive, aerospace,
telecommunications, and many other types of
embedded systems have a complete solution
to perform faster, more accurate and extensive
simulations of complex, large-scale systems.
double-precision floating point; single-precision floating point; signed and unsigned
8-, 16-, and 32-bit integers; and Boolean.
You can use Stateflow charts to include
supervisory control logic within your
Simulink model for activating or
deactivating conditionally executed
subsystems in Simulink. The Stateflow chart
receives input from the Simulink model,
determines the actions to be taken, changes
states appropriately, and sends logic signals to
activate or deactivate the triggered and
enabled subsystems in Simulink.
Analysis
Data Typing
Simulink supports complex numbers for
basic blocks and complex/real conversions.
In addition, the Data Type Conversion block
allows you to convert a signal of one type
(such as a float) to a signal of another type
(int32, for example).
Many of the blocks in Simulink support
several data types. The ability to specify the
data types of a model’s signal and block
parameters is particularly useful in realtime applications such as microcontrollers
and DSPs. With this capability, you can
specify the optimal data types required to
represent signals, block parameters, and
mathematical operations exactly as they are
represented on these devices. Additionally,
by choosing the appropriate data types for
your model’s signals and parameters, you
can dramatically increase the performance
and decrease the size of code generated
from the model. Supported data types are
Audits and Revision Histories
Simulink models are compatible with
standard configuration control software
such as SCCS and RCS. As a result, audits
and revision histories are easily maintained
for large projects and for models shared
within a multi-platform workgroup.
Simulink includes many features for
detailed system analysis. Key capabilities
include: linearization, equilibrium point
determination, animation, parameter
optimization, and parametric analysis.
Related Products
Simulink is the foundation for a family
of design solutions, spanning DSP,
communications, control, and power
system design.
Companion products include:
• Real-Time Workshop for code
generation
• Stateflow for event-driven systems
and logic design
• Simulink Performance Tools for
simulation acceleration and more
• Block libraries for specialized applications, such as the DSP Blockset,
the Fixed-Point Blockset, the Power
System Blockset, and the
Communications Blockset. ■
Extracting Linear Models
The dynamics of nonlinear block diagrams
can be approximated through linearization,
enabling you to apply design techniques
that require linear model representations.
You can use Simulink’s linmod function to
obtain linear state-space models from your
block diagrams.
For demos, application examples,
tutorials, user stories, and pricing:
Animation
• Visit www.mathworks.com
Simulink provides immediate access to
MATLAB’s powerful 2-D and 3-D graphics
and animation capabilities. You can use
MATLAB to enhance your visual displays
and gain deeper insight into your system’s
behavior as the simulation progresses.
• Contact The MathWorks directly
Integration with MATLAB
Because Simulink is built on top of MATLAB,
it provides a unique development environment.
This system allows you to run simulations
either interactively, using Simulink’s
graphical interface, or systematically, by
running sets of experiments in batch mode
from the MATLAB command line. You can
then generate test vectors and analyze the
results collectively.
US & Canada 508-647-7000
Benelux
France
Germany
Spain
Switzerland
UK
+31 (0)182 53 76 44
+33 (0)1 41 14 67 14
+49 (0)89 995901 0
+34 93 362 13 00
+41 (0)31 954 20 20
+44 (0)1223 423 200
Visit www.mathworks.com to obtain
contact information for authorized
MathWorks representatives in countries
throughout Asia Pacific, Latin America,
the Middle East, Africa, and the rest
of Europe.
9320v02 10/00
Control System Toolbox 5
for designing and analyzing automatic control systems
The Control System Toolbox builds on the
foundation of MATLAB® to provide specialized tools for control system engineering.
The toolbox is a collection of algorithms,
written primarily as M-files, that implement
common control system design, analysis,
and modeling techniques.
The Control System Toolbox is a core
toolbox for the analysis, design, and tuning
of feedback control systems. Its broad range
of capabilities encompasses both classical
and modern control design methods,
including root locus, pole placement, and
LQG regulator design. Convenient graphical
user interfaces (GUIs) simplify typical
control engineering tasks.
KEY FEATURES
■ LTI Viewer, an interactive GUI for analyzing and comparing
linear-time-invariant (LTI) systems
■ SISO Design Tool, an interactive GUI for analyzing and tuning
single-input/single-output (SISO) feedback control systems
■ GUI suite for setting preferences and properties, giving you
complete control over the visualization of time and frequency plots
■ Specialized data structures, called LTI objects, for concisely
representing transfer function, state-space, zero/pole/gain,
and frequency response data model formats
■ Support for multi-input/multi-output (MIMO) systems, continuous-time and sampled-data systems, and systems with time delays
■ Functions and operators for connecting LTI models with
complex block diagrams (series, parallel, and feedback
connections)
■ Support for a variety of discrete-to-continuous conversion
methods
■ Functions for plotting the time and frequency responses of
systems and comparing several systems with a single command
■ Tools for classical and modern control design techniques,
including root locus, loop shaping, pole placement, and
LQR/LQG regulation
A linked LTI Viewer
With the SISO Design GUI, you can tune gains and design compensators using
root locus and loop shaping techniques. The compensator parameters can be
changed graphically by interacting with the root locus and Bode diagrams.
When you modify the compensator gain or dynamics, the open- and closed-loop
response plots update automatically, providing useful guidance in the tuning process.
displays open- and
closed-loop response plots.
In the following example, sys1 and sys2 are
linear models being combined with a simple
command-line operation:
>> sys1 + sys2
% parallel connection of
systems sys1 and sys2
You can also manipulate and analyze entire
collections of linear models at the same time
using LTI model arrays.
Model Characteristics
The Control System Toolbox contains commands to query model characteristics such
as the I/O dimensions, poles, zeros, and DC
gain. These commands apply to both continuous- and discrete-time models.
Interconnecting Linear Models
You can easily connect LTI models in parallel,
series, or feedback mode. You can also use
these basic interconnections in combination
to derive models of complex block diagrams.
Analysis and Design
With the LTI Viewer, you can easily graph the responses of one or several systems—all in one window. Step and impulse
plots, pole/zero plots, and all frequency-domain responses (Bode, Nyquist, Nichols, and singular values) are available in the
LTI Viewer. The LTI Viewer allows you to display important response characteristics, such as stability margins on the plots using
data markers.
With the Control System Toolbox, you can
model linear-time-invariant (LTI) systems
in transfer function, zero/pole/gain, or
state-space form. You can manipulate both
continuous-time and discrete-time systems
and convert between various model representations. You can compute and graph time
responses, frequency responses, and root loci.
Other functions let you perform pole placement, optimal control, and estimation. The
Control System Toolbox is open and extensible, allowing you to create custom M-files to
suit your particular application.
Building Models
The Control System Toolbox supports four
linear model representations: state-space
models (SS), transfer functions (TF),
zero/pole/gain (ZPK) models, and frequency
response data (FRD) models.
LTI objects are provided for each model type.
In addition to model data, LTI objects can
store the sample time of discrete-time systems,
time delays, input and output names, notes
about the model, and more. Using LTI objects,
you can manipulate models as single entities
and combine them using matrix-like operations.
Some tasks lend themselves to graphical
manipulation, while others benefit from the
flexibility of the command line. The Control
System Toolbox is designed to accommodate
both approaches, providing both GUIs and a
complete set of command-line functions for
model analysis and design.
Analyzing Models Graphically Using the LTI Viewer
The Control System Toolbox LTI Viewer is a
GUI that simplifies the analysis of linear, timeinvariant systems. You use the LTI Viewer to
view and compare the response plots of several
linear models at the same time. You can generate time and frequency response plots to
inspect key response parameters, such as rise
time, maximum overshoot, and stability
margins. Using mouse-driven interactions
you can select input and output channels
from MIMO systems.
The LTI Viewer can display up to seven
different plot types simultaneously, including
step, impulse, Bode (magnitude and phase
or magnitude only), Nyquist, Nichols, sigma,
and pole/zero.
In addition to right-click menus, all response
plots include data markers. These allow you
to scan the plot data, identify key data, and
determine the source system for a given plot.
Using right-click menu options, you can
access several LTI Viewer controls and
options, including:
The LTI Viewer is suitable for a wide range
of applications where a GUI-driven environment is desirable. For situations that require
programming, customized plots, or the inclusion of data unrelated to your LTI models, the
Control System Toolbox provides commandline functions that implement the basic plots
for time- and frequency-domain analysis
used in control system engineering. These
functions apply to any kind of linear model
(continuous or discrete, SISO or MIMO,) or
to arrays of models.
• Plot Type—changes the plot type
• Systems—selects or deselects any of
the models loaded in the LTI Viewer
• Characteristics—displays key response
characteristics and parameters
• Zoom—zooms in and out of plot regions
• Grid—adds grids to your plots
• Properties—opens the Property Editor,
where you can customize plot attributes
Current Compensator
Analyzing Models Using the Command Line
Designing Compensators Using SISO Design Tool
The Control System Toolbox SISO Design
Tool is a GUI that lets you analyze and tune
SISO feedback control systems. Using the
SISO Design Tool, you can graphically tune
the compensator gain and dynamics using
a mix of root locus and loop shaping techniques. For example, you can use the root
locus view to stabilize the feedback loop and
enforce some minimum damping, and use
the Bode diagrams to adjust the bandwidth,
check the gain and phase margins, or add a
notch filter for disturbance rejection.
The SISO Design Tool is designed to work
closely with the LTI Viewer, allowing you to
rapidly iterate on your design and immediately see the results in the LTI Viewer. When
you make a change in the compensator, the
Feedback Structure
Open-Loop Bode Diagram
Gain Margin
Root Locus
Phase Margin
Status Bar
The SISO Design GUI can be used for both continuous- and discrete-time plants. Here, the root locus and Bode diagrams are shown for a discrete-time plant.
d
+
+
u
y
100
s 2 + s + 100
+
n
F(s)
yn
LQG Regulator
+
This example illustrates the design of a
simple LQG regulator. The code excerpt
below shows how the controller is
designed and how the closed-loop
Right-click menus
system is created. The impulse-response
simplify customizations
plot shows a comparison between the
of plots and GUIs
open-loop system (red) and the closedloop system (blue).
G = ss(tf(100,[1 1 100]))
Klqr = lqry(G,10,1)
Kest = kalman(G(:,[1 1]),1, 0.01)
F = lqgreg(Kest, Klqr)
clsys = feedback(G,F,+1)
impulse(G, 'r', clsys, 'b')
%
%
%
%
%
%
state-space plant model
design feedback gain matrix
Kalman estimator design
combine regulator and estimator
form closed-loop system
generate and plot impulse response
LTI Viewer associated with your SISO Design
Tool automatically updates the response plots
that you have chosen.
Pull-down and right-click menus give you
the flexibility to perform control design tasks
with one mouse-click. In particular, you can:
The SISO Design Tool:
• Drop compensator poles and zeros in
the root locus or Bode diagram views
• Add lead/lag networks and notch filters
• Graphically tune compensator parameters
with the mouse
• Inspect closed-loop responses (using the
LTI Viewer)
• Adjust phase and gain margins
• Convert models between discrete- and
continuous-time
• Integrates most Control System Toolbox
functionality into a single GUI
• Dynamically links time, frequency, and
pole/zero views, offering complementary
insights into the design objectives and issues
• Provides graphical insight into design
tradeoffs
• Helps manage complexity and design
iterations
Designing Compensators Using the Command Line
In addition to the SISO Design Tool, the
Control System Toolbox provides a set of
commands that you can use for a broader
range of control applications, including:
• Functions for classical SISO design
(damping data, root locus, and gain
and phase margins)
• Functions for modern MIMO design
(pole placement, LQR/LQG methods,
and Kalman filtering)
[Linear-Quadratic-Gaussian (LQG) control
is a modern state-space technique for
designing optimal dynamic regulators. It
enables you to trade off regulation performance and control effort and take into account
process disturbances and measurement
noise.]
Setting Plot Preferences and Properties
The Control System Toolbox provides three
GUIs that give you complete control over
the visualization of time and frequency plots
generated by the toolbox:
• Toolbox Preferences—global options
that you can save from session to session
• Tool Preferences—options set for a
particular instance of the LTI Viewer or
SISO Design Tool
• Plot Properties—options for customizing
a given response plot
Documentation and Demonstrations
The Control System Toolbox provides extensive online documentation, including Getting
Started, an introduction and tutorial for new
users, and complete reference chapters for
the toolbox GUIs and functions.
The Control System Toolbox also provides
an extensive suite of demonstrations, including tutorial demos (Getting Started, Model
Analysis, Do’s and Don’ts); interactive demos
(RLC circuit, stability margins, discretization);
and detailed case studies of six applications
(DC motor, feedback amplifier, disk drive,
jet autopilot, steel mill, and process control).
Related Products
The MathWorks provides several products
that are especially relevant to the tasks that
you can perform with the Control System
Toolbox. These include:
• Simulink®—a comprehensive environment
for modeling, simulating, and analyzing
dynamic systems in a block diagram
format
The Control System Toolbox Preferences dialog box allows you to specify options that are
saved from session to session. The LTI Viewer and SISO Design GUIs have preference dialog
boxes for setting plot options within these GUIs. Finally, individual response plots have property editors for further customization.
• Nonlinear Control Design Blockset—
an optimization-based approach to control
system design that tunes parameters based
on user-defined, time-domain performance
constraints
• System Identification Toolbox—tools for
building linear models of dynamic systems
from measured input/output data
• Fuzzy Logic Toolbox—tools for
developing fuzzy logic algorithms
• Robust Control Toolbox—tools for the
modeling, analysis, and design of “robust”
multivariable feedback control systems
using H∞ techniques
• µ-Analysis and Synthesis Toolbox—
computational algorithms for the structured
singular value, µ, applicable to robustness
and performance analysis for systems with
modeling and parameter uncertainties
• Linear Matrix Inequality Toolbox—
convex optimization algorithms for
solving linear matrix inequalities (LMI),
with application to robust control, multiobjective control, and gain scheduling
• Model Predictive Control Toolbox—
a complete set of tools for implementing
model predictive control strategies ■
Sample Commands
General
ctrlpref
damp
Set Control System Toolbox
preferences
Creating Linear Models
tf
zpk
ss, dss
frd
set
Create a transfer function
model
Create a zero/pole/gain model
Create a state-space model
Create a frequency response
data model
Set/modify properties of LTI
models
Data Extraction
tfdata
zpkdata
ssdata
get
frd
c2d
d2c
d2d
Conversion to state-space
Conversion to zero/pole/gain
Conversion to transfer function
Conversion to frequency data
Continuous-to-discrete
conversion
Discrete-to-continuous
conversion
Resample discrete-time model
parallel
series
feedback
lft
connect
Group LTI systems by
appending inputs and
outputs
Generalized parallel connection
Generalized series connection
Feedback connection of two
systems
Generalized feedback
inter-connection
Derive state-space model from
block diagram description
Model Dynamics
pole
zero
pzmap
ltiview
step
impulse
initial
System poles
System (transmission) zeros
Pole-zero map
Response analysis GUI
(LTI Viewer)
Step response
Impulse response
Response of state-space
system with given initial state
Response to arbitrary inputs
State-Space Models
rss, drss
ss2ss
ctrb, obsv
gram
minreal
ssbal
balreal
modred
Frequency-Domain Analysis
ltiview
bode
sigma
nyquist
nichols
margin
allmargin
freqresp
Response analysis GUI
(LTI Viewer)
Bode diagrams of the
frequency response
Singular value frequency plot
Nyquist plot
Nichols plot
Gain and phase margins
All crossover frequencies and
related gain/phase margins
Frequency response over a
frequency grid
Classical Design
sisotool
rlocus
System Interconnections
append
Time-Domain Analysis
lsim
Extract numerator(s) and
denominator(s)
Extract zero/pole/gain data
Extract state-space matrices
Access values of LTI model
properties
Conversions
ss
zpk
tf
dcgain
norm
covar
Natural frequency and
damping of system poles
DC (low frequency) gain
Norms of LTI systems
Covariance of response to
white noise
Time Delays
totaldelay
delay2z
pade
Total delay between each
input/output pair
Replace delays by poles at
z=0 or FRD phase shift
Pade approximation of
time delays
Matrix Equation Solvers
lyap
dlyap
care
SISO design GUI (root locus
and loop shaping techniques)
Evans root locus
Random stable statespace models
State coordinate
transformation
Controllability and
observability matrices
Controllability and
observability gramians
Minimal realization and
pole/zero cancellation
Diagonal balancing of
state-space realizations
Gramian-based
input/output balancing
Model state reduction
dare
Solve continuous Lyapunov
equations
Solve discrete Lyapunov
equations
Solve continuous algebraic
Riccati equations
Solve discrete algebraic
Riccati equations
Pole Placement
place
estim
reg
MIMO pole placement
Form estimator given
estimator gain
Form regulator given statefeedback and estimator gains
LQR/LQG Design
lqr, dlqr
lqry
lqrd
kalman
kalmd
Linear-quadratic (LQ) statefeedback regulator
LQ regulator with output
weighting
Discrete LQ regulator for
continuous plant
Kalman estimator
Discrete Kalman estimator
for continuous plant
US & Canada 508-647-7000
Benelux
France
Germany
Spain
Switzerland
UK
+31 (0)182 53 76 44
+33 (0)1 41 14 67 14
+49 (0)89 995901 0
+34 93 362 13 00
+41 (0)31 954 20 20
+44 (0)1223 423 200
of Europe.
9318v02 10/00
Neural Network Toolbox 4
for designing and simulating neural networks
The Neural Network Toolbox extends the
MATLAB® computing environment to
provide tools for the design, implementation,
visualization, and simulation of neural
networks. Neural networks are uniquely
powerful tools in applications where formal
analysis would be difficult or impossible,
such as pattern recognition and nonlinear
system identification and control. The
Neural Network Toolbox provides comprehensive support for many proven network
paradigms, as well as a graphical user interface that allows you to design and manage
your networks. The toolbox’s modular, open,
and extensible design simplifies the creation
of customized functions and networks.
KEY FEATURES
■
Graphical user interface (GUI) for creating, training, and simulating
your neural networks
■
Support for the most commonly used supervised and unsupervised
network architectures
■
A comprehensive set of training and learning functions
■
A suite of Simulink® blocks, as well as documentation and demonstrations of control-system applications
■
Automatic generation of Simulink models from neural network objects
■
Modular network representation, allowing an unlimited number
of input sets, layers, and network interconnections
■
Pre- and post-processing functions for improving network training
and assessing network performance
■
Routines for improving generalization
■
Visualization functions for viewing network performance
Working with Neural Networks
Inspired by the biological nervous system,
neural network technology is being used to
solve a wide variety of complex scientific,
engineering, and business problems.
Commercial applications include investment
portfolio trading, data mining, process
control, noise suppression, data compression,
and speech recognition. Neural networks are
ideally suited for such problems because, like
their biological counterparts, a neural
network can learn, and therefore can be
trained to find solutions, recognize patterns,
classify data, and forecast events.
Unlike analytical approaches commonly used
in fields such as statistics and control theory,
neural networks require no explicit model
and no limiting assumptions of normality or
linearity. The behavior of a neural network is
defined by the way its individual computing
elements are connected and by the strength
of those connections, or weights. The
weights are automatically adjusted by training the network according to a specified
learning rule until it properly performs the
desired task.
This window displays portions of the neural network
GUI. Dialogs and panes allow you to visualize your
network (top), evaluate training results (bottom),
and manage your networks (center).
The MathWorks
Supported Training Functions
Because neural networks require intensive
matrix computations, MATLAB provides a
natural framework for rapidly implementing neural networks and for studying their
behavior and application.
Neural Network Toolbox GUI
This tool lets you import potentially large
and complex data sets. The GUI also allows
you to create, initialize, train, simulate, and
manage your networks. Simple graphical
representations allow you to visualize and
understand network architecture.
Supported Network Architectures
Supervised Networks
Supervised neural networks are trained
to produce desired outputs in response to
example inputs, making them particularly
well suited for modeling and controlling
dynamic systems, classifying noisy data, and
predicting future events. The Neural
Network Toolbox supports the following
supervised networks:
• Feed-forward networks have one-way
connections from input to output layers.
They are commonly used for prediction,
pattern recognition, and nonlinear function
fitting. Supported feed-forward networks
include feed-forward backpropagation,
cascade-forward backpropagation,
feed-forward input-delay backpropagation,
linear, and perceptron networks.
• Radial basis networks provide an
alternative fast method for designing nonlinear feed-forward networks. Supported
variations include generalized regression
and probabilistic neural networks.
• Recurrent networks use feedback to
recognize both spatial and temporal
patterns. Supported recurrent networks
include Elman and Hopfield.
• Learning vector quantization (LVQ) is a
powerful method for classifying patterns
that are not linearly separable. LVQ allows
trainb
Batch training with weight and bias learning rules
trainbfg
BFGS quasi-Newton backpropagation
trainbr
Bayesian regularization
trainc
Cyclical order incremental update
traincgb
Powell-Beale conjugate gradient backpropagation
traincgf
Fletcher-Powell conjugate gradient backpropagation
traincgp
Polak-Ribiere conjugate gradient backpropagation
traingd
Gradient descent backpropagation
traingda
Gradient descent with adaptive learning rate (lr) backpropagation
traingdm
Gradient descent with momentum backpropagation
traingdx
Gradient descent with momentum & adaptive lr backpropagation
trainlm
Levenberg-Marquardt backpropagation
trainoss
One step secant backpropagation
trainr
Random order incremental update
trainrp
Resilient backpropagation (Rprop)
trains
Sequential order incremental update
trainscg
Scaled conjugate gradient backpropagation
you to specify class boundaries and the
granularity of classification.
Unsupervised Networks
Unsupervised neural networks are trained by
letting the network continually adjust itself to
new inputs. They find relationships within
data as it is presented and can automatically
define classification schemes. The Neural
Network Toolbox supports two types of selforganizing unsupervised networks:
• Competitive layers recognize and group
similar input vectors. By using these
groups, the network automatically sorts
the inputs into categories.
• Self-organizing maps learn to classify input
vectors according to similarity. Unlike
competitive layers, they also preserve the
topology of the input vectors, assigning
nearby inputs to nearby categories.
Supported Training and Learning Functions
Training and learning functions are
mathematical procedures used to automatically adjust the network’s weights and biases.
The training function dictates a global
algorithm that affects all the weights and
biases of a given network. The learning
function can be applied to individual weights
and biases within a network.
Supported Learning Functions
learncon
Conscience bias learning function
Control System Applications
learngd
Gradient descent weight/bias learning function
learngdm
Gradient descent with momentum weight/bias learning function
learnh
Hebb weight learning function
learnhd
Hebb with decay weight learning rule
Neural networks have been successfully
applied to the identification and control of
nonlinear systems. Included in the toolbox are
descriptions, demonstrations, and Simulink
blocks for three popular control applications:
model predictive control, feedback linearization, and model reference adaptive control.
learnis
Instar weight learning function
learnk
Kohonen weight learning function
learnlv1
LVQ1 weight learning function
learnlv2
LVQ2 weight learning function
learnos
Outstar weight learning function
learnp
Perceptron weight and bias learning function
learnpn
Normalized perceptron weight and bias learning function
learnsom
Self-organizing map weight learning function
learnwh
Widrow-Hoff weight and bias learning rule
Model Predictive Control Example
The following example shows the model
predictive control of a continuous stirred
tank reactor (CSTR). This controller creates
a neural network model of a nonlinear plant
to predict future plant response to potential
control signals. An optimization algorithm
then computes the control signals that
optimize future plant performance.
You can incorporate neural network control
blocks included in the toolbox into your
existing Simulink models. By changing the
parameters of these blocks you can tailor the
network’s performance to your application.
NN Predictive Controller
Reference
Optim.
Random Reference
Clock
Control
Signal
Plant
Output
Flow Rate
Concentration
NN
Model
Plant
(Continuous Stirred Tank Reactor)
X(2Y)
Graph
This window displays a Simulink model that includes the
neural network predictive control block and CSTR plant model
(top left). Dialogs and panes allow you to visualize validation
data (lower left) and manage the neural network control
block (lower center) and your plant identification (right).
1
p{1}
p{1}
a{1}
a{1}
Layer 1
p{1}
a{2}
a{2}
a{1}
Layer 2
a{1}
p{1}
a{1}
a{2}
1
TDL
weight
p{1}
Delays 1
IW{2,1}
2
TDL
weight
a{1}
Delays 2
LW{2,1}
1
a{3}
tansig
a{2}
bias
y{1}
a{2}
1
netsum
b{2}
Layer 3
Neural network simulation blocks for use in Simulink
can be automatically generated using the gensim
command. Here, a three-layer neural network has been
converted into Simulink blocks.
Simulink Support
Improving Generalization
Documentation and Examples
Once a network has been created and
trained, it can be easily incorporated into
Simulink models. A simple command
(gensim) automatically generates network
simulation blocks for use with Simulink.
This feature also makes it possible for you
to view your networks graphically.
Improving the network’s ability to generalize
helps prevent overfitting, a common problem
in neural network design. Overfitting occurs
when a network has memorized the training
set but has not learned to generalize to
new inputs. Overfitting produces a relatively
small error on the training set but will
produce a much larger error when new data
is presented to the network.
The Neural Network Toolbox User’s Guide
was written by Professor Emeritus Howard
Demuth and Mark Beale, developers of the
Neural Network Toolbox and authors, with
Professor Martin Hagen, of Neural Network
Design. The User’s Guide is of textbook
quality and provides a thorough treatment
of neural network architectures, paradigms,
and neural network applications. It also
includes a tutorial and application examples.
Additional demonstrations and application
examples are included with the product.
Pre- and Post-Processing Functions
Pre-processing the network inputs and
targets improves the efficiency of neural
network training. Post-processing enables
detailed analysis of network performance.
The Neural Network Toolbox provides the
following pre- and post-processing functions:
• Principal component analysis reduces
the dimensions of the input vectors.
• Post-training analysis performs a
regression analysis between the network
response and the corresponding targets.
• Scale minimum and maximum scales
inputs and targets so that they fall in the
range [-1,1].
• Scale mean and standard deviation
normalizes the mean and standard
deviation of the training set.
The MathWorks
The Neural Network Toolbox provides
two solutions to improve generalization:
• Regularization modifies the network’s
performance function, the measure of
error that the training process minimizes.
By changing it to include the size of the
weights and biases, training produces a
network that not only performs well with
the training data, but produces smoother
behavior when presented with new data.
• Early stopping is a technique that uses two
different data sets: the training set, which is
used to update the weights and biases, and
the validation set, which is used to stop
training when the network begins to overfit
the data.
US & Canada 508-647-7000
Benelux
France
Germany
Spain
Switzerland
UK
+31 (0)182 53 76 44
+33 (0)1 41 14 67 14
+49 (0)89 995901 0
+34 93 362 13 00
+41 (0)31 954 20 20
+44 (0)1223 423 200
of Europe.
8511v04 10/00
Signal Processing Toolbox 5
for algorithm development, signal and linear system analysis, and time-series modeling
The Signal Processing Toolbox is a collection
of MATLAB® functions that provides a
rich, customizable framework for analog
and digital signal processing (DSP). Graphical
user interfaces (GUIs) support interactive
designs and analyses, while command-line
functions support advanced algorithm development.
The Signal Processing Toolbox is the ideal
environment for signal analysis and DSP
algorithm development. It uses industrytested signal processing algorithms that
have been carefully chosen and implemented for maximum efficiency and
numeric reliability.
Signal Processing Toolbox functions are
implemented as M-files routines written in
the MATLAB language, which give you access
to the source code and algorithms. The opensystem philosophy of MATLAB and the
toolboxes enables you to make changes to
existing functions or add your own.
KEY FEATURES
■ A comprehensive set of signal and linear system models
■ Tools for analog filter design
■ Tools for Finite Impulse Response (FIR) and Infinite Impulse
Response (IIR) digital filter design, analysis, and implementation
■ The most widely used transforms, such as fast Fourier
transform (FFT) and discrete cosine transform (DCT)
■ Methods for spectrum estimation and statistical signal processing
■ Functions for parametric time-series modeling
■ Routines for waveform generation, including a Gaussian pulse
generator, a periodic sinc generator, and a pulse train generator
■ Data windowing algorithms
You can use the toolbox in speech and audio
processing, communications, digital control,
radar, geophysics, test instrumentation, realtime control, finance, medicine, and other
applications.
FDATool (above) is a built-in GUI that lets you design many types
of FIR and IIR filters. You select the filter types from the available
methods in the GUI. This diagram shows the GUI with Filter Design
Toolbox installed. The figure at left shows an annotatable print
preview of the filter’s magnitude response.
The MathWorks
Sample Functions
Filter Analysis
abs
angle
freqs
freqspace
freqz
freqzplot
grpdelay
impz
unwrap
zplane
fir1
Magnitude
Phase angle
Laplace transform
requency response
Frequency spacing for
frequency response.
Z-transform frequency
response
Plot frequency response
data
Group delay
Discrete impulse
response
Unwrap phase
Discrete pole-zero plot
Filter Implementation
conv
conv2
deconv
fftfilt
filter
filter2
filtfilt
filtic
latcfilt
medfilt1
sgolayfilt
sosfilt
upfirdn
Convolution
2-D convolution
Deconvolution
Overlap-add filter
implementation
Filter implementation
Two-dimensional digital
filtering
Zero-phase version
of filter
Determine filter initial
conditions
Lattice filter implementation
1-Dimensional median
filtering
Savitzky-Golay filter
implementation
Second-order sections
(biquad) filter implementation
Up sample, FIR filter,
down sample
cremez
fircls
fircls1
firls
firrcos
intfilt
kaiserord
remez
remezord
sgolay
Convolution matrix
Complex and nonlinear
phase equiripple FIR
filter design
Analog Lowpass Filter Prototypes
besselap
buttap
cheb1ap
cheb2ap
ellipap
butter
cheby1
cheby2
ellip
maxflat
yulewalk
Butterworth filter design
Chebyshev type I filter
design
Chebyshev type II filter
design
Elliptic filter design
Generalized Butterworth
lowpass filter design
Yule-Walker filter design
IIR Filter Order Estimation
buttord
cheb2ord
ellipord
Butterworth filter order
estimation
order estimation
order estimation
Elliptic filter order
estimation
Bessel filter prototype
Butterworth filter
prototype
prototype (passband
ripple)
prototype (stopband
ripple)
Elliptic filter prototype
Analog Filter Design
besself
butter
cheby1
cheby2
ellip
Bessel analog filter
design
Butterworth filter design
design
design
Elliptic filter design
Analog Filter Transformation
lp2bp
lp2bs
IIR Digital Filter Design
cheb1ord
FIR Filter Design
convmtx
fir2
Window based FIR
filter design - low, high,
band, stop, multi
FIR arbitrary shape
filter design using the
frequency sampling
method
Constrained Least
Squares filter design –
arbitrary response
Constrained Least
Squares FIR filter design
– low and highpass
Optimal least-squares
FIR filter design
Raised cosine FIR
filter design
Interpolation FIR
filter design
Kaiser window design
based filter order
estimation
Optimal Chebyshevnorm FIR filter design
Remez design based
filter order estimation
Savitzky-Golay FIR
smoothing filter design
lp2hp
lp2lp
Lowpass to bandpass
analog filter
transformation
Lowpass to bandstop
analog filter
transformation
Lowpass to highpass
analog filter
transformation
Lowpass to lowpass
analog filter
transformation
Filter Discretization
bilinear
impinvar
Bilinear transformation
with optional prewarping
Impulse invariance
analog to digital
conversion
Linear System Transformations
latc2tf
polystab
polyscale
Lattice or lattice ladder
to transfer function
conversion
Polynomial stabilization
Scale roots of polynomial
residuez
sos2ss
sos2tf
sos2zp
ss2sos
ss2tf
ss2zp
tf2latc
tf2sos
tf2ss
tf2zp
zp2sos
zp2ss
zp2tf
Z-transform partial fraction expansion
Second-order sections to
state-space conversion
transfer function conversion
zero-pole conversion
State-space to secondorder sections
conversion
State-space to transfer
function conversion
State-space to zero-pole
conversion
Transfer function to
lattice or lattice ladder
conversion
second-order sections
conversion
state-space conversion
zero-pole conversion
Zero-pole to secondorder sections
conversion
Zero-pole to state-space
conversion
Zero-pole to transfer
function conversion
fft
fft2
fftshift
hilbert
idct
ifft
ifft2
Cepstral Analysis
cceps
icceps
rceps
cohere
corrcoef
corrmtx
cov
csd
pburg
blackman
boxcar
chebwin
hamming
hann
kaiser
triang
Bartlett window
Blackman window
Rectangular window
Chebyshev window
Hamming window
Hanning window
Kaiser window
Triangular window
pcov
peig
periodogram
pmcov
Transforms
czt
dct
dftmtx
Chirp-z transform
Discrete cosine
transform
Discrete Fourier
transform matrix
pmtm
pmusic
pwelch
pyulear
rooteig
rootmusic
tfe
Complex cepstrum
Inverse complex
cepstrum
Real cepstrum and
minimum phase
reconstruction
Statistical Signal Processing
and Spectral Analysis
Windows
bartlett
Fast Fourier transform
2-D fast Fourier
transform
Swap vector halves
Discrete-time analytic
signal via Hilbert
transform
Inverse discrete cosine
transform
Inverse fast Fourier
transform
Inverse 2-D fast Fourier
transform
Coherence function
estimate
Correlation coefficients
Autocorrelation matrix
Covariance matrix
Cross spectral density
Power spectral density
estimate via Burg's
method
estimate via the covariance method
estimate via the eigenvector method
estimate via the
periodogram method
estimate via the
modified covariance
method
estimate via the
Thomson multitaper
method
xcorr
xcorr2
xcov
estimate via the MUSIC
method
estimate via Welch's
method
estimate via the YuleWalker AR Method
Sinusoid frequency and
power estimation via the
eigenvector algorithm
Sinusoid frequency and
power estimation via
the MUSIC algorithm
Transfer function
estimate
Cross-correlation
function
2-D cross-correlation
Covariance function
Parametric Modeling
arburg
arcov
armcov
aryule
invfreqs
invfreqz
prony
stmcb
AR parametric modeling
via Burg's method
via covariance method
via modified covariance
method
via the Yule-Walker
method
Analog filter fit to
frequency response
Discrete filter fit to
frequency response
Prony's discrete filter
fit to time response
Steiglitz-McBride
iteration for ARMA
modeling
Linear Prediction
ac2rc
ac2poly
is2rc
lar2rc
levinson
lpc
lsf2poly
poly2ac
poly2lsf
poly2rc
rc2ac
rc2is
rc2lar
rc2poly
rlevinson
schurrc
Multirate Signal Processing
Autocorrelation
sequence to reflection
coefficients conversion
Autocorrelation
sequence to prediction
polynomial conversion
Inverse sine parameters
to reflection coefficients
conversion
Log area ratios to reflection coefficients
conversion
Levinson-Durbin
recursion
Linear predictive
coefficients using autocorrelation method
Line spectral frequencies
to prediction polynomial conversion
Prediction polynomial
to autocorrelation
sequence conversion
to line spectral frequencies conversion
to reflection coefficients
conversion
Reflection coefficients to
autocorrelation
sequence conversion
inverse sine parameters
conversion
Reflection coefficients
to log area ratios
conversion
prediction polynomial
conversion
Reverse LevinsonDurbin recursion
Schur algorithm
decimate
interp
interp1
resample
spline
upfirdn
Resample data at a lower
sample rate
Resample data at a
higher sample rate
General 1-D
interpolation. (MATLAB
Toolbox)
Resample sequence
with new sampling rate
Cubic spline
interpolation
Up sample, FIR filter,
down sample
Waveform Generation
chirp
diric
gauspuls
gmonopuls
pulstran
rectpuls
sawtooth
sinc
square
tripuls
vco
Swept-frequency cosine
generator
Dirichlet (periodic sinc)
function
Gaussian RF pulse
generator
Gaussian monopulse
generator
Pulse train generator
Sampled aperiodic
rectangle generator
Sawtooth function
Sinc or sin(pi*x)/(pi*x)
function
Square wave function
Sampled aperiodic
triangle generator
Voltage controlled
oscillator
Specialized Operations
buffer
cell2sos
cplxpair
demod
Buffer a signal vector
into a matrix of data
frames
Convert cell array to
second-order-section
matrix
Order vector into
complex conjugate pairs
Demodulation for
communications
simulation
dpss
eqtflength
modulate
seqperiod
sos2cell
specgram
stem
strips
udecode
uencode
Discrete prolate
spheroidal sequences
(Slepian sequences)
Equalize the length
of a discrete-time
transfer function
Modulation for
communications
simulation
Find minimum-length
repeating sequence in
a vector
Convert second-ordersection matrix to cell
array
Spectrogram, for speech
signals
Plot discrete data
sequence
Strip plot
Uniform decoding of
the input
Uniform quantization
and encoding of the
input into N-bits
Graphical User Interfaces
fdatool
sptool
Filter Design and
Analysis Tool
Signal Processing Tool
Power Spectral Density Estimates for a 4th Order AR Model
40
MUSIC
Yule AR
Burg
Welch
MTM
Covariance
Mod Covar
Power Spectral Density (dB / rads/sample)
30
20
Spectral analysis of a signal
10
using a range of parametric and
nonparametric techniques.
0
-10
-20
The Signal Processing Toolbox provides
two GUIs:
FDATool is a comprehensive tool for
designing and analyzing digital filters
that helps you:
-30
-40
They are particularly useful for visualizing
time-frequency information, spectra, and
pole-zero locations. For example, you can
interactively design a filter by graphically
placing the poles and zeroes in the z-plane.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Normalized Angular Frequency (∗π rads/sample)
Signal and Linear System Models
The Signal Processing Toolbox provides a
broad range of models for representing
signals and linear time-invariant systems,
allowing you to choose the method that
best suits your application, including
representations for transfer functions state
space, and zero-pole-gain. The toolbox
also includes functions for transforming
models from one representation to another.
Filter Design
The Signal Processing Toolbox features a full
suite of design methods for finite impulse
response (FIR) and infinite impulse response
(IIR) digital filters. These methods support
the rapid design and evaluation of lowpass,
highpass, bandpass, bandstop, and multiband filters such as Butterworth, Chebyshev,
elliptic, Yule-Walker, window-based, leastsquares, and Parks-McClellan. The filter
structures available include the direct forms
I and II, lattice, lattice-ladder, and secondorder sections. You can comment among the
various realizations with tools provided.
Spectral Analysis
unsurpassed facilities for frequency-domain
analysis and spectral estimation. Several of
these methods are based on a highly optimized FFT. The toolbox includes functions
0.8
0.9
1
• Access most FIR and IIR filter design
methods in the toolbox using a
simplified, graphical interface
for computing the discrete Fourier, discrete
cosine, Hilbert, and other transforms useful
in analysis, coding, and filtering. The spectral
analysis methods available include Welch's,
Burg's, modified covariance, Yule-Walker, the
multitaper method, and the MUSIC method.
Visualization
The GUIs in the Signal Processing Toolbox
let you interactively view and measure
signals, design and apply filters, and perform
spectral analysis while exploring the effects
of different analysis parameters and methods.
• Analyze filters by exchanging magnitude,
phase, impulse, and step responses and by
calculating group delay and pole-zero plots
• Import previously designed filters and
filter coefficients that you have stored
in the MATLAB workspace. Export
filter coefficients
• Access additional filter design methods
and quantization features of the Filter
Design Toolbox (when that optional
product is installed)
• Print filter response directly from the
GUI with the option to annotate plots
SPTool’s Filter Designer includes a Pole/Zero editor
that lets you design a filter through the graphical
placement of poles and zeroes. The Filter Viewer
lets you view all characteristics of the filter.
• A crosshair locator that locates individual
data points on the spectrogram
You can evaluate time/frequency information in the spectrogram by using the signal
panner or the crosshair locator. This will
allow you to locate data points in the spectrogram. They will display and interactively
update a frequency slice of the input signal,
a time slice of signal, and a readout of time
and frequency values.
You can call the specgramdemo from
the MATLAB command line by typing
specgramdemo(y,FS) where y is the
input signal and Fs is the signal’s sampling
rate. Context-sensitive help is available
for specgramdemo.
Product Requirements
Specgramdemo is a user-friendly GUI that provides interactive
calculations of a signal’s time-frequency distribution.
SPTool is a suite of GUI tools providing
access to many of the signal, filter, and
spectral analysis functions that helps you:
• Measure and analyze the time-domain
information of one or more signals and
send audio signal to the PC’s sound card
• Graphically analyze frequency-domain
data using a variety of spectral estimation
methods, including Burg, FFT, multitaper
(MTM), MUSIC, eigenvector, Welch, and
Yule-Walker AR
An Interactive Demo
• Design and edit FIR and IIR filters
of various lengths and types and with
standard (lowpass, highpass, bandpass,
bandstop, and multiband) configurations, as well as design filters by
graphically placing poles and zeroes
in the z-plane
specgramdemo, a user-friendly GUI that
interactively calculates a signal’s time-frequency
distribution. Specgramdemo presents:
• View the characteristics of a designed or
imported filter, including its magnitude
response, phase response, group delay,
pole-zero plot, impulse response, and
step response
• The power spectral density of the
input signal
• Apply the filter to a selected signal
The MathWorks
The Signal Processing Toolbox runs on all
MathWorks supported platforms. It requires
MATLAB 6. ■
• The original time series data
• The spectrogram of the input signal
• A colorbar indicating the color scale
of the spectrogram
US & Canada 508-647-7000
Benelux
France
Germany
Spain
Switzerland
UK
+31 (0)182 53 76 44
+33 (0)1 41 14 67 14
+49 (0)89 995901 0
+34 93 362 13 00
+41 (0)31 954 20 20
+44 (0)1223 423 200
of Europe.
• A signal panner that lets you focus in and
out on the signal
9317v03 11/00
Wavelets Packets
The wavelet packets method is a generalization of wavelet decomposition that provides a richer range of decomposition encodings. The
following functions compute, decompose, and reconstruct wavelet
packets; construct and manipulate wavelet packet trees; and compute
entropy values and packet coefficients:
bestlevt
besttree
entrupd
wentropy
wp2wtree
wpcoef
wpcutree
wpdec
wpdec2
wpfun
wpjoin
wprcoef
wprec
wprec2
wpsplt
Best level tree (wavelet packet)
Best tree (wavelet packet)
Entropy update (wavelet packet)
Entropy (wavelet packet)
Extract wavelet tree from wavelet packet tree
Wavelet packet coefficients
Cut wavelet packet tree
Wavelet packet decomposition 1-D
Wavelet packet decomposition 2-D
Wavelet packet functions
Recompose wavelet packet
Reconstruct wavelet packet coefficients
Wavelet packet reconstruction 1-D
Wavelet packet reconstruction 2-D
Split (decompose) wavelet packet
Discrete Stationary Wavelet Transforms
iswt
iswt2
swt
swt2
Inverse discrete stationary wavelet transform 1-D
Inverse discrete stationary wavelet transform 2-D
Discrete stationary wavelet transform 1-D
Discrete stationary wavelet transform 2-D
De-noising and Compression for Signals and Images
The following functions specify coefficients and thresholds for denoising and compression and perform de-noising and compression
using wavelets or wavelet packets:
ddencmp
thselect
wbmpen
wdcbm
wdcbm2
wden
wdencmp
Default values for de-noising or compression
Threshold selection for de-noising
Penalized threshold for wavelet 1-D or
2-D de-noising
Thresholds for wavelet 1-D using
Birge-Massart strategy
Thresholds for wavelet 2-D
using Birge-Massart strategy
Automatic 1-D de-noising using wavelets
De-noising or compression using wavelets
The U.S. Federal Bureau of Investigation has selected wavelet compression techniques
for their extensive fingerprint database. Here, the automatic thresholding feature of
the Wavelet Toolbox produces a compressed image with about 72% zeros and 98% of
the original signal.
wnoise
wnoisest
wpbmpen
wpdencmp
wpthcoef
wthcoef
wthcoef2
wthresh
wthrmngr
Generate noisy wavelet test data
Estimate noise of 1-D wavelet coefficients
Penalized threshold for wavelet packet de-noising
De-noising or compression using wavelet packets
Wavelet packet coefficients thresholding
Wavelet coefficient thresholding 1-D
Wavelet coefficient thresholding 2-D
Perform soft or hard thresholding
Threshold settings manager
Utilities and Other Functions
The Wavelet Toolbox provides general mathematical, function,
matrix, and string manipulation utilities to support wavelet analysis.
Specialized tree management functions maintain and manipulate
data structures and tree structures, including creating and plotting
trees; specifying tree order and depth; and indexing, counting, and
reorganizing nodes.
Wavelet Analysis Applications
The Wavelet Toolbox supports a full suite
of wavelet analysis and synthesis operations.
It can be used to:
• Enhance edge detection in image processing
• Achieve high rates of signal or image
compression with virtually no loss of
significant data
• Restore noisy signals and degraded images
• Discover trends in noisy or faulty data
• Study the fractal properties of signals
and images
• Extract information-rich features for use
in classification and pattern-recognition
applications
The Wavelet Toolbox GUI provides
point-and-click access to power
wavelet-processing tasks, such as
Graphical User Interface
automatic de-noising, with instant
The GUI gives you all the functionality of
the toolbox in an intuitive, point-and-click
environment. It provides:
visualization of the results. Interval-
• Wavelet 1-D Tool for discrete wavelet
analysis of signals
• Wavelet 2-D Tool for discrete wavelet
analysis of images
• Continuous and Complex Continuous
Wavelet 1-D Tool for continuous wavelet
analysis of real signals using complex wavelets
• Signal and Image De-noising Tools, using
the stationary wavelet transform for performing translation-invariant de-noising
of signals
• Local Variance Adaptive Threshold Tools,
for defining time-dependent thresholds
• Density Estimation 1-D for estimating
wavelet-based density
• Regression Estimation 1-D for exploring
de-noising schemes for equally or unequally
sampled data
• Wavelet Coefficients Selection 1-D and
2-D for performing wavelet reconstruction
schemes based on various wavelet coefficient selection strategies
• Signal Extension/Truncation for performing one-dimensional signal extension and
truncation using periodic, symmetric,
smooth, and zeropadding methods
compression tools.
dependent threshold settings can also
be applied in the de-noising and
About the Authors
The authors of the Wavelet Toolbox are
Michel Misiti, Georges Oppenheim, and
Jean-Michel Poggi, mathematics professors at
École Centrale de Lyon, Université de MarneLa-Vallée, and Université René Descartes,
Paris 5, and Yves Misiti, a research engineer
specializing in Computer Sciences at
Université Paris-Sud.
The authors are members of the “Laboratoire
de Mathématique,” Université Paris-Sud.
Their fields of interest are wavelets, statistical
signal processing, stochastic processes, and
adaptive control. The authors’ group, which
was formed more than ten years ago, has
published numerous theoretical papers and
collaborated on many industrial applications
of advanced signal processing and control
technologies. ■
US & Canada 508-647-7000
Benelux
France
Germany
Spain
Switzerland
UK
+31 (0)182 53 76 44
+33 (0)1 41 14 67 14
+49 (0)89 995901 0
+34 93 362 13 00
+41 (0)31 954 20 20
+44 (0)1223 423 200
of Europe.
8797v02 10/00
MODULATION
CHANNELS
AWGN channel
Binary symmetric channel
Multipath Rayleigh fading channel
Rician fading channel
Digital Baseband and Passband Amplitude Modulation
General QAM modulator and demodulator
M-PAM modulator and demodulator
Rectangular QAM modulator and demodulator
SYNCHRONIZATION
Phase Modulation
Phase-locked loop
Baseband PLL
Charge pump PLL
Linearized baseband PLL
BPSK modulator and demodulator
DBPSK modulator and demodulator
DQPSK modulator and demodulator
M-DPSK modulator and demodulator
M-PSK modulator and demodulator
OQPSK modulator and demodulator
QPSK modulator and demodulator
BASIC COMM FUNCTIONS
1
2
3
4
5
1
1
2
2
3
Integrators
Discrete modulo integrator
Integrate and dump
Modulo integrator
Windowed integrator
Frequency Modulation
M-FSK modulator and demodulator
Continuous Phase Modulation
Sequence Operations
CPFSK modulator and demodulator
CPM modulator and demodulator
GMSK modulator and demodulator
MSK modulator and demodulator
Complex phase difference
Complex phase shift
Interlacer and deinterlacer
Repeat and derepeat
Puncture and insert zero
Scrambler and descrambler
Analog Baseband and Passband
DSB AM modulator and demodulator
DSBSC AM modulator and demodulator
FM modulator and demodulator
PM modulator and demodulator
SSB AM modulator and demodulator
UTILITY FUNCTIONS
101
5
dB
lin
Bit to integer converter
Data mapper
dB
Here a tutorial example shows how
you can quickly build a communication
Bernoulli bin
Convolutional
Encoder
system comprising a channel, modulation
Bernoulli Random
Binary Generator
Convolutional
Encoder
scheme, and coding.
Tx
Rx
BPSK
Unbuffer
BPSK
Modulator
Baseband
0.01203
Error Rate
Calculation
566
AWGN
Channel
4.706e+004
Error Rate Calculation
Display
Terminator
Info
Viterbi Decoder
Viterbi Decoder
Buffer
Terminator1
Scalar
quantizer
Re(u)
Sampled
Quantizer Encode
Complex to
Real-Imag
AWGN
Being based on Simulink, the Communications
Blockset handles arbitrarily complex systems
by allowing you to build and navigate
models hierarchically. You can process all
the multi-rate digital signals that are typical
in communications systems, such as frames,
bits and symbols. And you can make use of
Simulink’s continuous time features to
model analog signals.
Simulink provides the interactive,
block diagram simulation environment
including model construction, navigation,
simulation management and debugging.
It also provides primitive analog and
discrete, linear and non-linear building
blocks, such as arithmetic, logic and
relational operators, subsystems, Laplace
transforms, z-transforms, look-up tables,
polynomials and switches. You also have
the ability to add your own custom C code
or M code modules using the Simulink
S-function block. ■
This BER plot can be calculated by a MATLAB script that runs your simulation many times for
different Eb/No values.
USING THE COMMUNICATIONS BLOCKSET WITH OTHER MATHWORKS PRODUCTS
To run the Communications Blockset, the Communications Toolbox, the
Signal Processing Toolbox, Simulink and the DSP Blockset must also be installed.
The DSP Blockset This provides all the
MATLAB® With MATLAB you can create
key DSP blocks common in any digital
communications system. These blocks
include filters, adaptive filters, interpolation, signal operations, transforms, vector
math, matrix math, linear algebra, and
frequency scopes. The Communications
Blockset also makes extensive internal use
of the DSP Blockset.
scripts to automate the running of your
simulation multiple times to calculate
bit-error plots. You can also use it for post
processing of simulation data as well as
numerous ancillary parameter manipulation and generation tasks.
The Communication Toolbox This provides
a number of support functions for error
correction coding including polynomial
creation and Galois field computations.
The Communications Blockset also
makes extensive internal use of the
Communications Toolbox.
The MathWorks
Real-Time Workshop For large models or
long simulation runs, Real-time Workshop
can generate a standalone C executable
for running multiple simulations or for
co-simulation with low-level EDA tools.
US & Canada 508-647-7000
Benelux
France
Germany
Spain
Switzerland
UK
+31 (0)182 53 76 44
+33 (0)1 41 14 67 14
+49 (0)89 995901 0
+34 93 362 13 00
+41 (0)31 954 20 20
+44 (0)1223 423 200
of Europe.
Stateflow® You can also integrate your
physical layer design in Simulink and the
Communications Blockset with your linklayer design in Stateflow, The MathWorks
control logic design product.
9869v00 10/00

MatLab versión 6 - Facultade de Informática

Transcripción

Documentos relacionados

Modern life can turn into a blur

Or Castrol EDGE

Document

810-0133 Crystal Water Cart Ins

Oil Change Specials!

Image Processing