Your SlideShare is downloading. ×
  • Like
computational intelligence paradigms theory & applications using matlab  2010
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Now you can save presentations on your phone or tablet

Available for both IPhone and Android

Text the download link to your phone

Standard text messaging rates apply

computational intelligence paradigms theory & applications using matlab 2010

  • 4,613 views
Published

book S. Sumathi, Surekha Paneerselvam Computational Intelligence Paradigms Theory & Applications using MATLAB 2010.pdf

book S. Sumathi, Surekha Paneerselvam Computational Intelligence Paradigms Theory & Applications using MATLAB 2010.pdf

Published in Education , Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
4,613
On SlideShare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
411
Comments
0
Likes
2

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. ComputationalIntelligenceParadigmsTheory andApplicationsusing MATLAB®© 2010 by Taylor and Francis Group, LLC
  • 2. S. sumathisurekha p.ComputationalIntelligenceParadigmsTheory andApplicationsusing MATLAB®A C H A P M A N & H A L L B O O KCRC Press is an imprint of theTaylor & Francis Group, an informa businessBoca Raton London New York© 2010 by Taylor and Francis Group, LLC
  • 3. MATLAB ® and Simulink ® are trademarks of the Math Works, Inc. and are used with permission. The Mathworks doesnot warrant the accuracy of the text or exercises in this book. This book’s use or discussion of MATLAB ® and Simulink® software or related products does not constitute endorsement or sponsorship by the Math Works of a particular peda-gogical approach or particular use of the MATLAB ® and Simulink ® software.CRC PressTaylor & Francis Group6000 Broken Sound Parkway NW, Suite 300Boca Raton, FL 33487-2742© 2010 by Taylor and Francis Group, LLCCRC Press is an imprint of Taylor & Francis Group, an Informa businessNo claim to original U.S. Government worksPrinted in the United States of America on acid-free paper10 9 8 7 6 5 4 3 2 1International Standard Book Number: 978-1-4398-0902-0 (Hardback)This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have beenmade to publish reliable data and information, but the author and publisher cannot assume responsibility for the valid-ity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyrightholders of all material reproduced in this publication and apologize to copyright holders if permission to publish in thisform has not been obtained. If any copyright material has not been acknowledged please write and let us know so we mayrectify in any future reprint.Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or uti-lized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopy-ing, microfilming, and recording, or in any information storage or retrieval system, without written permission from thepublishers.For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923,978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. Fororganizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged.Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only foridentification and explanation without intent to infringe.Library of Congress Cataloging‑in‑Publication DataSumathi, S., 1968‑Computational intelligence paradigms : theory & applications using MATLAB / S. Sumathi andSurekha Paneerselvam.p. cm.Includes bibliographical references and index.ISBN 978‑1‑4398‑0902‑0 (hard back : alk. paper)1. Computational intelligence. 2. MATLAB. I. Paneerselvam, Surekha, 1980‑ II. Title.Q342.S94 2010006.3‑‑dc22 2009022113Visit the Taylor & Francis Web site athttp://www.taylorandfrancis.comand the CRC Press Web site athttp://www.crcpress.com© 2010 by Taylor and Francis Group, LLC
  • 4. ContentsPreface xvii1 Computational Intelligence 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Primary Classes of Problems for CI Techniques . . . . 51.2.1 Optimization . . . . . . . . . . . . . . . . . . . 51.2.2 NP-Complete Problems . . . . . . . . . . . . . 61.3 Neural Networks . . . . . . . . . . . . . . . . . . . . . 71.3.1 Feed Forward Neural Networks . . . . . . . . . 81.4 Fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . 91.4.1 Fuzzy Sets . . . . . . . . . . . . . . . . . . . . 91.4.2 Fuzzy Controllers . . . . . . . . . . . . . . . . 111.5 Evolutionary Computing . . . . . . . . . . . . . . . . . 121.5.1 Genetic Algorithms . . . . . . . . . . . . . . . 131.5.2 Genetic Programming . . . . . . . . . . . . . . 141.5.3 Evolutionary Programming . . . . . . . . . . . 151.5.4 Evolutionary Strategies . . . . . . . . . . . . . 151.6 Swarm Intelligence . . . . . . . . . . . . . . . . . . . . 161.7 Other Paradigms . . . . . . . . . . . . . . . . . . . . . 171.7.1 Granular Computing . . . . . . . . . . . . . . 181.7.2 Chaos Theory . . . . . . . . . . . . . . . . . . 201.7.3 Artificial Immune Systems . . . . . . . . . . . 211.8 Hybrid Approaches . . . . . . . . . . . . . . . . . . . . 221.9 Relationship with Other Paradigms . . . . . . . . . . . 231.10 Challenges To Computational Intelligence . . . . . . . 25Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Review Questions . . . . . . . . . . . . . . . . . . . . . . . . 272 Artificial Neural Networks with MATLAB 292.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 292.2 A Brief History of Neural Networks . . . . . . . . . . . 292.3 Artificial Neural Networks . . . . . . . . . . . . . . . . 322.3.1 Comparison of Neural Network to the Brain . 332.3.2 Artificial Neurons . . . . . . . . . . . . . . . . 33v© 2010 by Taylor and Francis Group, LLC
  • 5. vi2.3.3 Implementation of Artificial Neuron Electroni-cally . . . . . . . . . . . . . . . . . . . . . . . . 352.3.4 Operations of Artificial Neural Network . . . . 372.3.5 Training an Artificial Neural Network . . . . . 402.3.6 Comparison between Neural Networks, Tradi-tional Computing, and Expert Systems . . . . 442.4 Neural Network Components . . . . . . . . . . . . . . 462.4.1 Teaching an Artificial Neural Network . . . . . 522.4.2 Learning Rates . . . . . . . . . . . . . . . . . . 542.4.3 Learning Laws . . . . . . . . . . . . . . . . . . 552.4.4 MATLAB Implementation of Learning Rules . 61Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Review Questions . . . . . . . . . . . . . . . . . . . . . . . . 673 Artificial Neural Networks - Architectures and Algo-rithms 693.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 693.2 Layered Architectures . . . . . . . . . . . . . . . . . . 713.2.1 Single-Layer Networks . . . . . . . . . . . . . . 713.2.2 Multilayer Networks . . . . . . . . . . . . . . . 733.3 Prediction Networks . . . . . . . . . . . . . . . . . . . 753.3.1 The Perceptron . . . . . . . . . . . . . . . . . 753.3.2 MATLAB Implementation of a Perceptron Net-work . . . . . . . . . . . . . . . . . . . . . . . 793.3.3 Feedforward Back-Propagation Network . . . . 833.3.4 Implementation of BPN Using MATLAB . . . 913.3.5 Delta Bar Delta Network . . . . . . . . . . . . 953.3.6 Extended Delta Bar Delta . . . . . . . . . . . 973.3.7 Directed Random Search Network . . . . . . . 1003.3.8 Functional Link Artificial Neural Network(FLANN) or Higher-Order Neural Network . . 102Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Review Questions . . . . . . . . . . . . . . . . . . . . . . . . 1064 Classification and Association Neural Networks 1094.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 1094.2 Neural Networks Based on Classification . . . . . . . . 1094.2.1 Learning Vector Quantization . . . . . . . . . 1104.2.2 Implementation of LVQ in MATLAB . . . . . 1154.2.3 Counter-Propagation Network . . . . . . . . . 1164.2.4 Probabilistic Neural Network . . . . . . . . . . 1234.2.5 Implementation of the Probabilistic Neural NetUsing MATLAB . . . . . . . . . . . . . . . . . 128© 2010 by Taylor and Francis Group, LLC
  • 6. vii4.3 Data Association Networks . . . . . . . . . . . . . . . 1294.3.1 Hopfield Network . . . . . . . . . . . . . . . . 1304.3.2 Implementation of Hopfield Network in MAT-LAB . . . . . . . . . . . . . . . . . . . . . . . . 1334.3.3 Boltzmann Machine . . . . . . . . . . . . . . . 1344.3.4 Hamming Network . . . . . . . . . . . . . . . . 1374.3.5 Bi-Directional Associative Memory . . . . . . . 1414.4 Data Conceptualization Networks . . . . . . . . . . . . 1464.4.1 Adaptive Resonance Network . . . . . . . . . . 1464.4.2 Implementation of ART Algorithm in MATLAB 1514.4.3 Self-Organizing Map . . . . . . . . . . . . . . . 1534.5 Applications Areas of ANN . . . . . . . . . . . . . . . 1584.5.1 Language Processing . . . . . . . . . . . . . . 1594.5.2 Character Recognition . . . . . . . . . . . . . . 1604.5.3 Data Compression . . . . . . . . . . . . . . . . 1604.5.4 Pattern Recognition . . . . . . . . . . . . . . . 1604.5.5 Signal Processing . . . . . . . . . . . . . . . . 1614.5.6 Financial . . . . . . . . . . . . . . . . . . . . . 161Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162Review Questions . . . . . . . . . . . . . . . . . . . . . . . . 1625 MATLAB Programs to Implement Neural Networks 1655.1 Illustration 1: Coin Detection Using Euclidean Distance(Hamming Net) . . . . . . . . . . . . . . . . . . . . . . 1655.2 Illustration 2: Learning Vector Quantization - ClusteringData Drawn from Different Regions . . . . . . . . . . 1715.3 Illustration 3: Character Recognition Using KohonenSom Network . . . . . . . . . . . . . . . . . . . . . . . 1745.4 Illustration 4: The Hopfield Network as an AssociativeMemory . . . . . . . . . . . . . . . . . . . . . . . . . . 1825.5 Illustration 5: Generalized Delta Learning Rule andBack-Propagation of Errors for a Multilayer Network . 1875.6 Illustration 6: Classification of Heart Disease UsingLearning Vector Quantization . . . . . . . . . . . . . . 1895.7 Illustration 7: Neural Network Using MATLAB Simulink 198Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200Review Questions . . . . . . . . . . . . . . . . . . . . . . . . 2006 MATLAB-Based Fuzzy Systems 2036.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2036.2 Imprecision and Uncertainty . . . . . . . . . . . . . . 2056.3 Crisp and Fuzzy Logic . . . . . . . . . . . . . . . . . . 2056.4 Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . 207© 2010 by Taylor and Francis Group, LLC
  • 7. viii6.5 Universe . . . . . . . . . . . . . . . . . . . . . . . . . . 2096.6 Membership Functions . . . . . . . . . . . . . . . . . . 2106.6.1 Types of Membership Functions . . . . . . . . 2126.6.2 Membership Functions in the MATLAB FuzzyLogic Toolbox . . . . . . . . . . . . . . . . . . 2146.6.3 MATLAB Code to Simulate Membership Func-tions . . . . . . . . . . . . . . . . . . . . . . . 2176.6.4 Translation of Parameters between MembershipFunctions Using MATLAB . . . . . . . . . . . 2236.7 Singletons . . . . . . . . . . . . . . . . . . . . . . . . . 2246.8 Linguistic Variables . . . . . . . . . . . . . . . . . . . 2256.9 Operations on Fuzzy Sets . . . . . . . . . . . . . . . . 2256.9.1 Fuzzy Complements . . . . . . . . . . . . . . . 2276.9.2 Fuzzy Intersections: t-norms . . . . . . . . . . 2306.9.3 Fuzzy Unions: t-conorms . . . . . . . . . . . . 2336.9.4 Combinations of Operations . . . . . . . . . . 2356.9.5 MATLAB Codes for Implementation of FuzzyOperations . . . . . . . . . . . . . . . . . . . . 2366.9.6 Aggregation Operations . . . . . . . . . . . . . 2396.10 Fuzzy Arithmetic . . . . . . . . . . . . . . . . . . . . . 2426.10.1 Arithmetic Operations on Intervals . . . . . . 2436.10.2 Arithmetic Operations on Fuzzy Numbers . . 2446.10.3 Fuzzy Arithmetic Using MATLAB Fuzzy LogicToolbox . . . . . . . . . . . . . . . . . . . . . . 2466.11 Fuzzy Relations . . . . . . . . . . . . . . . . . . . . . . 2476.12 Fuzzy Composition . . . . . . . . . . . . . . . . . . . . 2516.12.1 MATLAB Code to Implement Fuzzy Composi-tion . . . . . . . . . . . . . . . . . . . . . . . . 254Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260Review Questions . . . . . . . . . . . . . . . . . . . . . . . . 2607 Fuzzy Inference and Expert Systems 2617.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2617.2 Fuzzy Rules . . . . . . . . . . . . . . . . . . . . . . . . 2617.2.1 Generation of Fuzzy Rules . . . . . . . . . . . 2627.2.2 Disintegration of Rules . . . . . . . . . . . . . 2627.2.3 Aggregation of Rules . . . . . . . . . . . . . . 2637.3 Fuzzy Expert System Model . . . . . . . . . . . . . . . 2647.3.1 Fuzzification . . . . . . . . . . . . . . . . . . . 2647.3.2 Fuzzy Rule Base and Fuzzy IF-THEN Rules . 2657.3.3 Fuzzy Inference Machine . . . . . . . . . . . . 2667.3.4 Defuzzification . . . . . . . . . . . . . . . . . . 267© 2010 by Taylor and Francis Group, LLC
  • 8. ix7.3.5 Implementation of Defuzzification using MAT-LAB Fuzzy Logic Toolbox . . . . . . . . . . . 2737.4 Fuzzy Inference Methods . . . . . . . . . . . . . . . . 2767.4.1 Mamdani’s Fuzzy Inference Method . . . . . . 2787.4.2 Takagi–Sugeno Fuzzy Inference Method . . . . 2817.4.3 Tsukamoto Fuzzy Inference Method . . . . . . 2827.5 Fuzzy Inference Systems in MATLAB . . . . . . . . . 2837.5.1 Mamdani-Type Fuzzy Inference . . . . . . . . 2867.5.2 Sugeno-Type Fuzzy Inference . . . . . . . . . 2927.5.3 Conversion of Mamdani to Sugeno System . . 2957.6 Fuzzy Automata and Languages . . . . . . . . . . . . 2977.7 Fuzzy Control . . . . . . . . . . . . . . . . . . . . . . . 2987.7.1 Fuzzy Controllers . . . . . . . . . . . . . . . . 3007.7.2 A Fuzzy Controller in MATLAB . . . . . . . . 302Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305Review Questions . . . . . . . . . . . . . . . . . . . . . . . . 3058 MATLAB Illustrations on Fuzzy Systems 3078.1 Illustration 1: Application of Fuzzy Controller UsingMATLAB — Fuzzy Washing Machine . . . . . . . . . 3078.2 Illustration 2 - Fuzzy Control System for a Tanker Ship 3178.3 Illustration 3 - Approximation of Any Function UsingFuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . 3368.4 Illustration 4 - Building Fuzzy Simulink Models . . . . 343Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348Review Questions . . . . . . . . . . . . . . . . . . . . . . . . 3489 Neuro-Fuzzy Modeling Using MATLAB 3519.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3519.2 Cooperative and Concurrent Neuro-Fuzzy Systems . . 3529.3 Fused Neuro-Fuzzy Systems . . . . . . . . . . . . . . . 3529.3.1 Fuzzy Adaptive Learning Control Network(FALCON) . . . . . . . . . . . . . . . . . . . . 3539.3.2 Adaptive Neuro-Fuzzy Inference System (AN-FIS) . . . . . . . . . . . . . . . . . . . . . . . . 3539.3.3 Generalized Approximate Reasoning-Based In-telligent Control (GARIC) . . . . . . . . . . . 3559.3.4 Neuro-Fuzzy Control (NEFCON) . . . . . . . 3569.3.5 Fuzzy Inference and Neural Network in FuzzyInference Software (FINEST) . . . . . . . . . . 3609.3.6 Fuzzy Net (FUN) . . . . . . . . . . . . . . . . 3629.3.7 Evolving Fuzzy Neural Network (EFuNN) . . . 363© 2010 by Taylor and Francis Group, LLC
  • 9. x9.3.8 Self–Constructing Neural Fuzzy Inference Net-work (SONFIN) . . . . . . . . . . . . . . . . . 3649.3.9 Evolutionary Design of Neuro-Fuzzy Systems . 3649.4 Hybrid Neuro-Fuzzy Model — ANFIS . . . . . . . . . 3679.4.1 Architecture of Adaptive Neuro-Fuzzy InferenceSystem . . . . . . . . . . . . . . . . . . . . . . 3679.4.2 Hybrid Learning Algorithm . . . . . . . . . . 3709.5 Classification and Regression Trees . . . . . . . . . . . 3729.5.1 CART — Introduction . . . . . . . . . . . . . 3729.5.2 Node Splitting Criteria . . . . . . . . . . . . . 3739.5.3 Classification Trees . . . . . . . . . . . . . . . 3749.5.4 Regression Trees . . . . . . . . . . . . . . . . . 3759.5.5 Computational Issues of CART . . . . . . . . . 3759.5.6 Computational Steps . . . . . . . . . . . . . . 3769.5.7 Accuracy Estimation in CART . . . . . . . . . 3799.5.8 Advantages of Classification and RegressionTrees . . . . . . . . . . . . . . . . . . . . . . . 3809.6 Data Clustering Algorithms . . . . . . . . . . . . . . . 3829.6.1 System Identification Using Fuzzy Clustering . 3829.6.2 Hard C-Means Clustering . . . . . . . . . . . . 3839.6.3 Fuzzy C-Means (FCM) Clustering . . . . . . . 3869.6.4 Subtractive Clustering . . . . . . . . . . . . . . 3889.6.5 Experiments . . . . . . . . . . . . . . . . . . . 389Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393Review Questions . . . . . . . . . . . . . . . . . . . . . . . . 39310 Neuro-Fuzzy Modeling Using MATLAB 39510.1 Illustration 1 - Fuzzy Art Map . . . . . . . . . . . . . 39510.2 Illustration 2: Fuzzy C-Means Clustering — Compara-tive Case Study . . . . . . . . . . . . . . . . . . . . . . 40210.3 Illustration 3 - Kmeans Clustering . . . . . . . . . . . 40310.4 Illustration 4 - Neuro-Fuzzy System Using Simulink . 41110.5 Illustration 5 - Neuro-Fuzzy System Using Takagi–Sugeno and ANFIS GUI of MATLAB . . . . . . . . . 414Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417Review Questions . . . . . . . . . . . . . . . . . . . . . . . . 41811 Evolutionary Computation Paradigms 41911.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 41911.2 Evolutionary Computation . . . . . . . . . . . . . . . 42011.3 Brief History of Evolutionary Computation . . . . . . 42211.4 Biological and Artificial Evolution . . . . . . . . . . . 42311.4.1 Expressions Used in Evolutionary Computation 423© 2010 by Taylor and Francis Group, LLC
  • 10. xi11.4.2 Biological Evolution Inspired by Nature . . . . 42311.4.3 Evolutionary Biology . . . . . . . . . . . . . . 42511.5 Flow Diagram of a Typical Evolutionary Algorithm . 42811.6 Models of Evolutionary Computation . . . . . . . . . . 43011.6.1 Genetic Algorithms (GA) . . . . . . . . . . . . 43011.6.2 Genetic Programming (GP) . . . . . . . . . . . 43111.6.3 Evolutionary Programming (EP) . . . . . . . . 43511.6.4 Evolutionary Strategies (ESs) . . . . . . . . . 43611.7 Evolutionary Algorithms . . . . . . . . . . . . . . . . . 43911.7.1 Evolutionary Algorithms Parameters . . . . . 44211.7.2 Solution Representation . . . . . . . . . . . . . 44211.7.3 Fitness Function . . . . . . . . . . . . . . . . . 44311.7.4 Initialization of Population Size . . . . . . . . 44411.7.5 Selection Mechanisms . . . . . . . . . . . . . . 44411.7.6 Crossover Technique . . . . . . . . . . . . . . . 45111.7.7 Mutation Operator . . . . . . . . . . . . . . . 45511.7.8 Reproduction Operator . . . . . . . . . . . . . 45811.8 Evolutionary Programming . . . . . . . . . . . . . . . 45911.8.1 History . . . . . . . . . . . . . . . . . . . . . . 45911.8.2 Procedure of Evolutionary Programming . . . 46011.8.