E. S. Gopi-Algorithm Collections for Digital Signal Processing Applications using Matlab -Springer (

E. S. Gopi-Algorithm Collections for Digital Signal Processing Applications using Matlab -Springer (

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A C.I.P. Catalogue record for this book is available from the Library of Congress. 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. Printed on acid-free paper 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.
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This book is dedicated to my Wife G.Viji and my Son V.G.Vasig
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Contents Preface xiii Acknowledgments x Chapter 1 ARTIFICIAL INTELLIGENCE 1 Particle Swarm Algorithm 1 1-1 How are the Values of ‘x’ and ‘y’ are Updated in Every Iteration? 2 1-2 PSO Algorithm to Maximize the Function F(X, Y, Z) 4 1-3 M-program for PSO Algorithm 6 1-4 Program Illustration 8 2 Genetic Algorithm 9 2-1 Roulette Wheel Selection Rule 10 2-2 Example 11 2-2-1 M-program for genetic algorithm 11 2-2-2 Program illustration 13 2-3 Classification of Genetic Operators 15 2-3-1 Simple crossover 16 2-3-2 Heuristic crossover 16 2-3-3 Arith crossover 17 3 Simulated Annealing 18 3-1 Simulated Annealing Algorithm 19 3-2 Example 19 3-3 M-program for Simulated Annealing 23 v vii
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4 Back Propagation Neural Network 24 4-1 Single Neuron Architecture 25 4-2 Algorithm 27 4-3 Example 29 4-4 M-program for Training the Artificial Neural Network for the Problem Proposed in the Previous Section 31 5 Fuzzy Logic Systems 32 5-1 Union and Intersection of Two Fuzzy Sets 32 5-2 Fuzzy Logic Systems 33 5-2-1 Algorithm 35 5-3 Why Fuzzy Logic Systems? 38 5-4 Example 39 5-5 M-program for the Realization of Fuzzy Logic System for the Specifications given in Section 5-4 41 6 Ant Colony Optimization 44 6-1 Algorithm 44 6-2 Example 48 6-3 M-program for Finding the Optimal Order using Ant Colony Technique for the Specifications given in the Section 6-2 50 Chapter 2 PROBABILITY AND RANDOM PROCESS 1 Independent Component Analysis 53 1-1 ICA for Two Mixed Signals 53 1-1-1 ICA algorithm 62 1-2 M-file for Independent Component Analysis 65 2 Gaussian Mixture Model 68 2-1 Expectation-maximization Algorithm 70 2-1-1 Expectation stage 71 2-1-2 Maximization stage 71 2-2 Example 72 2-3 Matlab Program 73 2-4 Program Illustration 76 3 K-Means Algorithm for Pattern Recognition 77 3-1 K-means Algorithm 77 3-2 Example 77 3-3 Matlab Program for the K-means Algorithm Applied for the Example given in Section 3-2 78 4 Fuzzy K-Means Algorithm for Pattern Recognition 79 4-1 Fuzzy K-means Algorithm 80 4-2 Example 81 4-3 Matlab Program for the Fuzzy k-means Algorithm Applied for the Example given in Section 4-2 83 viii Contents
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5 Mean and Variance Normalization 84 5-1 Algorithm 84 5-2 Example 1 85 5-3 M-program for Mean and Variance Normalization 86 Chapter 3 NUMERICAL LINEAR ALGEBRA 87 1 Hotelling Transformation 87
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  • Spring '19
  • Dr T V Rama Krishna
  • Particle swarm optimization

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