Algorithm Collections for Digital Signal Processing Applications Using Matlab
Algorithm Collections for Digital Signal Processing Applications Using Matlab
E.S. Gopi
National Institute of Technology, Tiruchi, India
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ISBN 978-1-4020-6409-8 (HB) ISBN 978-1-4020-6410-4 (e-book)
Published by Springer, P.O. Box 17, 3300 AA Dordrecht, The Netherlands. www.springer.com
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All Rights Reserved 2007 Springer No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work.
This book is dedicated to my Wife G.Viji and my Son V.G.Vasig
Contents
Preface Acknowledgments Chapter 1 ARTIFICIAL INTELLIGENCE 1 Particle Swarm Algorithm 1-1 How are the Values of 'x' and 'y' are Updated in Every Iteration 1-2 PSO Algorithm to Maximize the Function F(X, Y, Z) 1-3 M-program for PSO Algorithm 1-4 Program Illustration 2 Genetic Algorithm 2-1 Roulette Wheel Selection Rule 2-2 Example 2-2-1 M-program for genetic algorithm 2-2-2 Program illustration 2-3 Classification of Genetic Operators 2-3-1 Simple crossover 2-3-2 Heuristic crossover 2-3-3 Arith crossover 3 Simulated Annealing 3-1 Simulated Annealing Algorithm 3-2 Example 3-3 M-program for Simulated Annealing
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4 Back Propagation Neural Network 4-1 Single Neuron Architecture 4-2 Algorithm 4-3 Example 4-4 M-program for Training the Artificial Neural Network for the Problem Proposed in the Previous Section 5 Fuzzy Logic Systems 5-1 Union and Intersection of Two Fuzzy Sets 5-2 Fuzzy Logic Systems 5-2-1 Algorithm 5-3 Why Fuzzy Logic Systems 5-4 Example 5-5 M-program for the Realization of Fuzzy Logic System for the Specifications given in Section 5-4 6 Ant Colony Optimization 6-1 Algorithm 6-2 Example 6-3 M-program for Finding the Optimal Order using Ant Colony Technique for the Specifications given in the Section 6-2 Chapter 2 PROBABILITY AND RANDOM PROCESS 1 Independent Component Analysis 1-1 ICA for Two Mixed Signals 1-1-1 ICA algorithm 1-2 M-file for Independent Component Analysis 2 Gaussian Mixture Model 2-1 Expectation-maximization Algorithm 2-1-1 Expectation stage 2-1-2 Maximization stage 2-2 Example 2-3 Matlab Program 2-4 Program Illustration 3 K-Means Algorithm for Pattern Recognition 3-1 K-means Algorithm 3-2 Example 3-3 Matlab Program for the K-means Algorithm Applied for the Example given in Section 3-2 4 Fuzzy K-Means Algorithm for Pattern Recognition 4-1 Fuzzy K-means Algorithm 4-2 Example 4-3 Matlab Program for the Fuzzy k-means Algorithm Applied for the Example given in Section 4-2

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