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Application Toolboxes

One of the key features of Simulink is that it is built on top of MATLAB. As a result, Simulink users have direct access to the wide range of MATLAB-based tools for generating, analyzing, and optimizing systems implemented in Simulink. These tools include MATLAB Application Toolboxes, specialized collections of M-files for working on particular classes of problems.

Toolboxes are more than just collections of useful functions; they represent the efforts of some of the world's top researchers in fields such as controls, signal processing, and system identification. MATLAB Application Toolboxes therefore let you "stand on the shoulders" of world class scientists.

All toolboxes are built using MATLAB. This has some very important implications for you:

Here is a list of professional toolboxes currently available from The MathWorks. This list is by no means static-- more are being created every year.

The Communications Toolbox..    The Communications Toolbox provides an integrated set of tools for accelerating the design, analysis, and simulation of modern communications systems. It combines MATLAB's high-level language with the ease of use of Simulink's block diagram interface, and provides communications engineers with comprehensive communications system design and analysis capabilities. The toolbox is useful in such diverse industries as telecommunications, telephony, aerospace, and computer peripherals.

The Control System Toolbox.    The Control System Toolbox, the foundation of the MATLAB control design toolbox family, contains functions for modeling, analyzing, and designing automatic control systems. The application of automatic control grows each year as sensors and computers become less expensive. As a result, automatic controllers are used not only in highly technical settings for automotive and aerospace systems, computer peripherals, and process control, but also in less obvious applications such as washing machines and cameras.

The Financial Toolbox..    The Financial Toolbox operates with MATLAB to provide a robust set of financial functions essential to financial and quantitative analysis. Applications include pricing securities, calculating interest and yield, analyzing derivatives, and optimizing portfolios. The Financial Toolbox requires the Statistics and Optimization Toolboxes. The Simulink graphical interface is recommended for Monte Carlo and non-stochastic simulations for pricing fixed-income securities, derivatives, and other instruments.

The Frequency-Domain System Identification Toolbox.    The Frequency-Domain System Identification Toolbox by István Kollár, in cooperation with Johan Schoukens and researchers at the Vrije Universiteit in Brussels, is a set of M-files for modeling linear systems based on measurements of the system's frequency response.

The Fuzzy Logic Toolbox.    The Fuzzy Logic Toolbox provides a complete set of GUI-based tools for designing, simulating, and analyzing fuzzy inference systems. Fuzzy logic provides an easily understandable, yet powerful way to map an input space to an output space with arbitrary complexity, with rules and relationships specified in natural language. Systems can be simulated in MATLAB or incorporated into a Simulink block diagram, with the ability to generate code for stand-alone execution.

The Higher-Order Spectral Analysis Toolbox.    The Higher-Order Spectral Analysis Toolbox, by Jerry Mendel, C. L. (Max) Nikias, and Ananthram Swami, provides tools for signal processing using higher-order spectra. These methods are particularly useful for analyzing signals originating from a nonlinear process or corrupted by non-Gaussian noise.

The Image Processing Toolbox.    The Image Processing Toolbox contains tools for image processing and algorithm development. It includes tools for filter design and image restoration; image enhancement; analysis and statistics; color, geometric, and morphological operations; and 2-D transforms.

The LMI Control Toolbox..    The LMI Control Toolbox, authored by leading researchers: Pascal Gahinet, Arkadi Nemirovski, and Alan Laub, allows one to efficiently solve Linear Matrix Inequalities (LMIs). LMIs are special convex optimization problems that arise in many disciplines, including control, identification, filtering, structural design, graph theory, and linear algebra.

The LMI Control Toolbox also features a variety of LMI-based tools for control systems design and covers applications such as robust stability and performance analysis, robust gain scheduling, and multi-objective controller synthesis with a mix of H-infinity, LQG, and pole placement objectives.

The Model Predictive Control Toolbox.    The Model Predictive Control Toolbox was written by Manfred Morari and N. Lawrence Ricker. Model predictive control is especially useful for control applications with many input and output variables, many of which have constraints. As a result, it has become particularly popular in chemical engineering and other process control applications.

The Mu-Analysis and Synthesis Toolbox.    The Mu-Analysis and Synthesis Toolbox, by Gary Balas, Andy Packard, John Doyle, Keith Glover, and Roy Smith, contains specialized tools for H optimal control, and µ-analysis and synthesis, an approach to advanced robust control design of multivariable linear systems.

The NAG Foundation Toolbox.    The NAG Foundation Toolbox includes more than 200 numeric computation functions from the well-regarded NAG Fortran subroutine libraries. It provides specialized tools for boundary-value problems, optimization, adaptive quadrature, surface and curve-fitting, and other applications.

The Neural Network Toolbox.    The Neural Network Toolbox by Howard Demuth and Mark Beale is a collection of MATLAB functions for designing and simulating neural networks. Neural networks are computing architectures, inspired by biological nervous systems, that are useful in applications where formal analysis is extremely difficult or impossible, such as pattern recognition and nonlinear system identification and control.

The Optimization Toolbox.    The Optimization Toolbox contains commands for the optimization of general linear and nonlinear functions, including those with constraints. An optimization problem can be visualized as trying to find the lowest (or highest) point in a complex, highly contoured landscape. An optimization algorithm can thus be likened to an explorer wandering through valleys and across plains in search of the topographical extremes.

The Partial Differential Equation Toolbox..    The Partial Differential Equation Toolbox extends the MATLAB Technical Computing Environment for the study and solution of PDEs in two space dimensions (2-D) and time. The PDE Toolbox provides a set of command line functions and an intuitive graphical user interface for preprocessing, solving, and postprocessing generic 2-D PDEs using the Finite Element Method (FEM). The toolbox also provides automatic and adaptive meshing capabilities and a suite of eight application modes for common PDE application areas such as heat transfer, structural mechanics, electrostatics, magnetostatics, and diffusion. These application areas are common in the fields of engineering and physics.

The QFT Control Design Toolbox.    The Quantitative Feedback Theory Toolbox by Yossi Chait, Craig Borghesani, and Oded Yaniv implements QFT, a frequency-domain approach to controller design for uncertain systems that provides direct insight into the trade-offs between controller complexity (hence the ability to implement it) and specifications.

The Robust Control Toolbox.    The Robust Control Toolbox provides a specialized set of tools for the analysis and synthesis of control systems that are "robust" with respect to uncertainties that can arise in the real world. The Robust Control Toolbox was created by controls theorists Richard Y. Chiang and Michael G. Safonov.

The Signal Processing Toolbox.    The Signal Processing Toolbox contains tools for signal processing. Applications include audio (e.g., compact disc and digital audio tape), video (digital HDTV, image processing, and compression), telecommunications (fax and voice telephone), medicine (CAT scan, magnetic resonance imaging), geophysics, and econometrics.

The Spline Toolbox.    The Spline Toolbox by Carl de Boor, a pioneer in the field of splines, provides a set of M-files for constructing and using splines, which are piecewise polynomial approximations. Splines are useful because they can approximate other functions without the unwelcome side effects that result from other kinds of approximations, such as piecewise linear curves.

The Statistics Toolbox.    The Statistics Toolbox provides a set of M-files for statistical data analysis, modeling, and Monte Carlo simulation, with GUI-based tools for exploring fundamental concepts in statistics and probability.

The Symbolic Math Toolbox.    The Symbolic Math Toolbox gives MATLAB an integrated set of tools for symbolic computation and variable-precision arithmetic, based on Maple V®. The Extended Symbolic Math Toolbox adds support for Maple programming plus additional specialized functions.

The System Identification Toolbox.    The System Identification Toolbox, written by Lennart Ljung, is a collection of tools for estimation and identification. System identification is a way to find a mathematical model for a physical system (like an electric motor, or even a financial market) based only on a record of the system's inputs and outputs.

The Wavelet Toolbox..    The Wavelet Toolbox provides a comprehensive collection of routines for examining local, multiscale, or nonstationary phenomena. Wavelet methods offer additional insight and performance in any application where Fourier techniques have been used. The toolbox is useful in many signal and image processing applications, including speech and audio processing, communications, geophysics, finance, and medicine.


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