Neural Network Systems Techniques and Applications. Volume 6. Fuzzy Logic and Expert Systems Applications PDF
Preview Neural Network Systems Techniques and Applications. Volume 6. Fuzzy Logic and Expert Systems Applications
Fuzzy Logic and Expert Systems Applications Neural Network Systems Techniques and Applications Edited by Cornelius T. Leondes VOLUME 1. Algorithms and Architectures VOLUME 2. optimization Techniques VOLUME 3. Implementation Techniques VOLUME 4. Industrial and Manufacturing Systems VOLUME 5. Image Processing and Pattern Recognition VOLUME 6. Fuzzy Logic and Expert Systems Applications VOLUME 7. Control and Dynamic Systems Fuzzy Logic and Expert Systems Applications Edited by Cornelius T. Leondes Professor Emeritus University of California Los Angeles, California VOLUME 6 OF Neural Network Systems Techniques and Applications ACADEMIC PRESS San Diego London Boston New York Sydney Tokyo Toronto This book is printed on acid-free paper, fe) Copyright © 1998 by ACADEMIC PRESS All Rights Reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Academic Press a division of Harcourt Brace & Company 525 B Street, Suite 1900, San Diego, California 92101-4495, USA http://www.apnet.com Academic Press Limited 24-28 Oval Road, London NWl 7DX, UK http://www.hbuk.co.uk/ap/ Library of Congress Card Catalog Number: 97-80441 International Standard Book Number: 0-12-443866-0 PRINTED IN THE UNITED STATES OF AMERICA 97 98 99 00 01 02 ML 9 8 7 6 5 4 3 2 1 Contents Contributors xiii Preface xv Fuzzy Neural Networks Techniques and Their Applications Hisao Ishibuchi and Manabu Nil I. Introduction 1 II. Fuzzy Classification and Fuzzy Modeling by Nonfuzzy Neural Networks 6 A. Fuzzy Classification and Fuzzy Modeling 6 B. Learning for Fuzzy Classification 9 C. Learning for Fuzzy Modeling 21 III. Interval-Arithmetic-Based Neural Networks 27 A. Interval Arithmetic in Neural Networks 27 B. Neural Networks for Handling Interval Inputs 30 C. Neural Networks with Interval Weights 36 rV. Fuzzified Neural Networks 40 A. Fuzzy Arithmetic in Neural Networks 40 B. Neural Networks for Handling Fuzzy Inputs 42 C. Neural Networks with Fuzzy Weights 47 V. Conclusion 51 References 52 vi Contents Implementation of Fuzzy Systems Chu Kzvong Chak, Gang Feng, and Marimuthu Palaniswami I. Introduction 57 II. Structure of Fuzzy Systems for Modeling and Control 60 A. Fuzzy Sets and Fuzzy Logic 60 B. Basic Structure of Fuzzy Systems for Modeling and Control 62 C. Types of Fuzzy Systems for Modeling and Control 62 D. Input Domain and Output Domain 64 E. Rule Base 64 F. Input Fuzzy Partitions 66 G. AND Matrix for Input Fuzzy Terms and Input Fuzzy Regions 68 H. Output Fuzzy Partitions 69 I. OR Matrix for Input Fuzzy Regions and Output Fuzzy Regions 69 J. Fuzzification 71 K. Inference Engine 71 L. Defuzzification 75 M. Concluding Remarks 76 III. Design 1: A Fuzzy Neural Network with an Additional OR Layer 76 A. Introduction 76 B. Input Dimensional Space Partitioning 77 C. Structure of the Fuzzy System 78 D. Architecture of the Proposed Neural Network 80 E. Hybrid Learning Algorithm 82 F. Simulation Examples 86 G. Concluding Remarks 93 IV. Design 2: A Fuzzy Neural Network Based on Hierarchical Space Partitioning 94 A. Introduction 94 B. Hierarchical Input Space Partitioning 94 C. Structure of the Fuzzy System 97 D. Architecture of Proposed Fuzzy Neural Network 100 E. Learning Algorithm 102 F. Simulation Examples HI G. Concluding Remarks 117 V. Conclusion 117 Appendix 118 References 120 Contents v Neural Networks and Rule-Based Systems Aldo Aiello, Ernesto Burattini, and Guglielmo Tamburrini I. Introduction 123 II. Nonlinear Thresholded Artificial Neurons 124 III. Production Rules 125 rV. Forward Chaining 127 V. Chunking 132 A. Chunking and Production Systems 132 B. Neural Module for Chunking 134 C. Selecting Indexes 138 VI. Neural Tools for Uncertain Reasoning: Toward Hybrid Extensions 140 A. Transforming Excitation Values into Sequences of Firings 141 B. Transforming Sequences of Firings into Excitation Values 141 C. Product of Positive Integers 143 VII. Qualitative and Quantitative Uncertain Reasoning 145 A. Preconditions in Nonmonotonic Inference 145 B. Qualitative Hypothesis Selection in Two-Level Causal Networks 152 C. Query Processes and the Probabilistic Causal Method 156 VIII. Purely Neural, Rule-Based Diagnostic System 158 A. Abduction-Prediction Cycle 158 B. Diagnoses in Pediatric Gastroenterology 160 C. Neural Implementation 163 IX. Conclusions 171 References 173 Construction of Rule-Based Intelligent Systems Graham P. Fletcher and Chris J. Hinde I. Introduction 175 II. Representation of a Neuron 176 III. Converting Neural Networks to Boolean Functions 179 A. Boolean Representation of a Natural Neuron 180 B. Boolean Representation of a Real Neuron 182 C. Examples of Boolean Function Derivation 184 Contents TV. Example Application of Boolean Rule Extraction 185 V. Network Design, Pruning, and Weight Decay 187 A. Network Design 188 B. System Investigation 190 C. Segmentation of System Variables 190 D. Boolean Structure 190 E. Pruning and Weight Decay 190 VI. Simplifying the Derived Rule Base 192 VII. Example of the Construction of a Rule-Based Intelligent System 197 VIII. Using Rule Extraction to Verify the Networks 202 A. Applying Simple Image Enhancement Techniques to Rules 204 B. Using Enhancement to Explain the Action of the Pole-Balancing Network 206 IX. Conclusions 208 References 209 Expert Systems in Soft Computing Paradigm Sankar K. Pal and Sushmita Mitra I. Introduction 211 U. Expert Systems: Some Problems and Relevance of Soft Computing 214 A. Role of Fuzzy Logic 216 B. Use of Connectionist Models 219 C. Need for Integrating Fuzzy Logic and Neural Networks 221 D. Utility of Knowledge-Based Networks 223 III. Connectionist Expert Systems: A Review 225 IV. Neuro-Fuzzy Expert Systems 227 A. Ways of Integration 227 B. Various Methodologies 229 C. Using Fuzzy Knowledge-Based Networks 233 V. Other Hybrid Models 234 A. Rough Sets 234 B. Genetic Algorithms 236 VI. Conclusions 237 References 237 Contents ix Mean-Value-Based Functional Reasoning Techniques in the Development of Fuzzy-Neural Network Control Systems Keigo Watanabe and Spyros G. Tzafestas I. Introduction 243 II. Fuzzy Reasoning Schemes 245 A. Input-Data-Based Functional Reasoning 245 B. Simplified Reasoning 246 C. Mean-Value-Based Functional Reasoning 247 III. Design of the Conclusion Part in Functional Reasoning 248 A. Input-Data-Based Functional Reasoning 248 B. Mean-Value-Based Functional Reasoning 248 IV. Fuzzy Gaussian Neural Networks 249 A. Construction 249 B. Number of Learning Parameters 253 C. Training 255 V. Attitude Control Application Example 258 A. Two-Input-Single-Output Reasoning 258 B. Three-Input-Single-Output Reasoning 265 VI. Mobile Robot Example 270 A. Model of a Mobile Robot 270 B. Simulation Examples 271 VII. Conclusions 281 References 282 Fuzzy Neural Network Systems in Model Reference Control Systems Yie-Chien Chen and Ching-Cheng Teng I. Introduction 285 II. Fuzzy Neural Network 286 A. Fuzzy Inference System 286 B. Structure of the Fuzzy Neural Network 290 C. Layered Operation of the Fuzzy Neural Network 292 D. Supervised Learning 294 E. Initialization of the Fuzzy Neural Network 297
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