Neural Network Systems Techniques and Applications. Volume 6. Fuzzy Logic and Expert Systems Applications

437 Pages·15.9 MB·English

by  Leondes C.T. (Ed.)

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
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Copyright © 1998 by ACADEMIC PRESS
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Library of Congress Card Catalog Number: 97-80441
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PRINTED IN THE UNITED STATES OF AMERICA
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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