Neural Network Theory and MATLAB7 Implementation

Author: Feis-tech Technology Research and Development Center
Publisher:
Publish Date: 2005-12-01
Features:
● Basics of MATB7 and neural networks
● Neural network toolbox functions and examples for various types
● Theories of various neural networks and MATLAB implementation
● Neural network control theory and application design
● Graphical User Interface (GUI) of neural networks
● Fault diagnosis based on neural networks
● Prediction based on neural networks
● Fuzzy control based on neural networks
● Adaptive noise cancellation technology based on neural networks.
Target Audience: This book can serve as a supplementary textbook for senior undergraduate and graduate students in science and engineering disciplines to study neural networks, or as a reference for scientists and engineers engaged in research and application in this field. It is part of the "MATLAB Application Technology" series and is based on the newly released Neural Network Toolbox 4.0.3 in MATLAB7. The first two chapters introduce the basics of MATLAB7 and neural networks, providing a detailed classification and introduction of important functions in the neural network toolbox, along with complete examples. From Chapter 3 to Chapter 5, several important types of neural networks, including perceptrons, linear networks, and BP networks, are introduced, covering their structures, learning algorithms, and MATLAB implementation methods. Chapter 6 covers the graphical user interface of neural networks. The last five chapters discuss how to use the neural network toolbox to solve practical problems in application domains such as control, fault diagnosis, prediction, and active noise cancellation.
Target Audience: This book can serve as a supplementary textbook for senior undergraduate and graduate students in science and engineering disciplines to study neural networks, or as a reference for scientists and engineers engaged in research and application in this field. It is part of the "MATLAB Application Technology" series and is based on the newly released Neural Network Toolbox 4.0.3 in MATLAB7. The first two chapters introduce the basics of MATLAB7 and neural networks, providing a detailed classification and introduction of important functions in the neural network toolbox, along with complete examples. From Chapter 3 to Chapter 5, several important types of neural networks, including perceptrons, linear networks, and BP networks, are introduced, covering their structures, learning algorithms, and MATLAB implementation methods. Chapter 6 covers the graphical user interface of neural networks. The last five chapters discuss how to use the neural network toolbox to solve practical problems in application domains such as control, fault diagnosis, prediction, and active noise cancellation.