Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms

363 Pages·1997·15.44 MB·English

by  Hideyuki Takagi (auth.)

INTELLIGENT HYBRID SYSTEMS:
FUZZY LOGIC,
NEURAL NETWORKS,
AND GENETIC ALGORITHMS
INTELLIGENT HYBRID SYSTEMS:
FUZZY LOGIC,
NEURAL NETWORKS,
AND GENETIC ALGORITHMS
EDITED BY
Da Ruan
Belgian Nuc/ear Research Centre (SCKeCEN)
Mol, Belgium
....
"
SPRINGER SCIENCE+BUSINESS MEDIA, LLC
ISBN 978-1-4613-7838-9 ISBN 978-1-4615-6191-0 (eBook)
DOI 10.1007/978-1-4615-6191-0
Library of Congress Cataloging-in-Publication Data
A C.I.P. Catalogue record for this book is available
from the Library of Congress.
Copyright © 1997 by Springer Science+Business Media New York
Originally published by Kluwer Academic Publishers in 1997
Softcover reprint of the hardcover 1s t edition 1997
All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system or transmitted in any form or by any means, mechanical, photo
copying, recording, or otherwise, without the prior written permission of the
publisher, Springer Science+Business Media, LLC.
Printed on acid-free paper.
CONTENTS
CONTRIBUTORS xiii
FOREWORD
Paul P. Wang xv
EDITOR'S PREFACE
Da Ruan xvii
Part 1: BASIC PRINCIPLES AND METHODOLOGIES
1 INTRODUCTION TO FUZZY SYSTEMS, NEURAL
NETWORKS, AND GENETIC ALGORITHMS
Hideyuki Takagi 3
1 Introduction 3
2 What are fuzzy systems 4
3 What are neural networks 9
4 What are genetic algorithms 17
5 Models and applications of cooperative systems 24
REFERENCES 31
2 A FUZZY NEURAL NETWORK FOR APPROXIMATE
FUZZY REASONING
Liam P. Maguire, T. Martin McGinnity, and Liam ]. McDaid 35
1 Introduction 35
2 Fuzzy reasoning and the proposed fuzzy neural network 37
3 The applications and determination of the fuzzy neural
network parameters 40
vi Intelligent Hybrid Systems: FL, NN, and GA
4 The implementation results 53
REFERENCES 56
3 NOVEL NEURAL ALGORITHMS FOR SOLVING
FUZZY RELATION EQUATIONS
Xiaozhong Li and Da Ruan 59
1 Introduction 60
2 Max-min operator networks and fuzzy 8 rule 62
3 Theoretical results 68
4 Fuzzy bidirectional associative memory 73
5 Max-times operator networks 75
6 An extended fuzzy neuron and network 77
7 Relationship with t-norm and t-conorm 80
8 A novel training algorithm- extended fuzzy 8 rule I 81
9 Simulation results 84
10 Conclusions and future work 85
REFERENCES 88
4 METHODS FOR SIMPLIFICATION OF
FUZZY MODELS
Uzay Kaymak, Robert Babu§ka, Magne Setnes,
Henk B. Verbruggen, Hans R. van Nauta Lemke 91
1 Introduction 91
2 Fuzzy modeling 92
3 Cluster validity 95
4 Compatible cluster merging 98
5 Similarity based rule base simplification 101
6 Conclusions 107
REFERENCES 107
5 A NEW APPROACH OF NEUROFUZZY LEARNING
ALGORITHM
Masaharu Mizumoto and Yan Shi 109
1 Introduction 109
2 Conventional neurofuzzy learning algorithm 110
Contents vii
3 A new approach of neurofuzzy learning algorithm for
tuning fuzzy rules 115
4 Numerical examples 119
5 Conclusions 126
REFERENCES 129
Part 2: DATA ANALYSIS AND INFORMATION SYSTEMS
6 NEURAL NETWORKS IN INTElliGENT
DATA ANALYSIS
Xiaohui Liu 133
1 Introduction 133
2 IDA at Birkbeck 135
3 Self-organising maps and back propagation 140
4 Data cleaning 142
5 Knowledge-based outlier analysis 144
6 Data exploration and knowledge discovery 147
7 Integration 152
8 Concluding remarks 156
REFERENCES 157
7 DATA-DRIVEN IDENTIFICATION OF
KEY VARIABLES
Bo Yuan and George Klir 161
1 Introduction 162
2 Cluster analysis 164
3 Evolutionary algorithms 174
4 Identifying key variables by an evolutionary
fuzzy c-means algorithm 177
5 Identifying key variable by fuzzy measure and
fuzzy integrals 182
REFERENCES 186
viii Intelligent Hybrid Systems: FL, NN, and GA
8 APPLICATIONS OF INTElliGENT TECHNIQUES IN
PROCESS ANALYSIS
Joachim Angstenberger and Richard Weber 189
1 Introduction 189
2 Applications of intelligent techniques in
process industry 190
3 Software tools 205
4 Conclusions 207
REFERENCES 207
9 NEUROFUZZY-CHAOS ENGINEERING FOR
BUILDING INTElliGENT ADAPTIVE
INFORMATION SYSTEMS
Nikola K. Kasabov and Robert Kozma 209
1 Introduction 209
2 FuNN-a fuzzy neural network model for adaptive
learning and monitoring of knowledge 210
3 Using fractal theory for analysing dynamic processes 215
4 A methodology for building adaptive FuNN-based
multimodular systems 218
5 Adaptive intelligent systems for chaotic time-series
prediction and control 222
6 Conclusions 228
REFERENCES 229
10 A SEQUENTIAL TRAINING STRATEGY FOR
LOCALLY RECURRENT NEURAL NETWORKS
Jie Zhang and A. Julian Morris 231
1 Introduction 232
2 Locally recurrent neural networks 234
3 Sequential orthogonal training 235
4 Mixed node locally recurrent neural networks 242
5 Applications 244
6 Conclusions 248
REFERENCES 251
Contents ix
Part 3: NONLINEAR SYSTEMS AND SYSTEM
IDENTIFICATION
11 ADAPTIVE GENETIC PROGRAMMING FOR
SYSTEM IDENTIFICATION
Andreas Bastian 255
1 Introduction 255
2 A brief introduction into genetic programming 256
3 Adaptive genetic programming 267
4 Application examples 276
5 Outlook and conclusion 280
REFERENCES 281
12 NONUNEAR SYSTEM IDENTIFICATION WITH
NEUROFUZZY METHODS
Oliver Nelles 283
1 Introduction 283
2 Fuzzy models 284
3 Local linear model trees 290
4 Identification of a combustion engine turbocharger 300
5 Conclusions 307
REFERENCES 308
13 A GENETIC ALGORITHM FOR MIXED-INTEGER
OPTIMISATION IN POWER AND WATER SYSTEM
DESIGN AND CONTROL
Kai Chen, Ian C. Parmee, and Chris R. Gane 311
1 Introduction 312
2 The optimisation problem 314
3 A hybrid GA-SLP solver 316
4 A design application in a nuclear station 320
5 Optimal control of a water supply system 324
6 Conclusions 328
REFERENCES 329
x Intelligent Hybrid Systems: FL, NN, and GA
14 SOFT COMPUTING BASED SIGNAL PREDICTION,
RESTORATION, AND FILTERING
Eiji Uchino and Takeshi Yamakawa 331
1 Introduction 331
2 Restoration of a damaged signal by neo-fuzzy-neuron 332
3 Filtering of a noisy signal by RBF network 340
4 Conclusions 348
REFERENCES 350
SUBJECT INDEX 353
CONTRIBUTORS
Joachim Angstenberger George Klir
Management Intelligenter Technologien Center for Intelligent Systems and
GmbH Dept of Systems Science & Ind. Eng.
Aachen, Germany Binghamton University-SUNY
Binghamton, New York, USA
Robert Babuska
Dept of Electrical Engineering Robert Kozma
Delft University of Technology Dept of Information Science
Delft, the Netherlands University of Otago
Dunedin, New Zealand
Andreas Bastian
Electronic Research Xiaozhong Li
Volkswagen AG Nuclear Research Centre (SCK' CEN)
Wolfsburg, Germany Mol, Belgium
Kai Chen Xiaohui Liu
Plymouth Engineering Centre Dept of Computer Science
Plymouth University University of London, Birkbeck College
Plymouth, United Kingdom London, United Kingdom
Chris R. Gane Liam P. Maguire
Nuclear Technology Branch Intelligent Systems Engineering Lab.
Nuclear Electric Ltd University of Ulster, Magee College
Gloucester, United Kingdom Derry, United Kingdom
Nikola K. Kasabov Liam J. McDaid
Dept of Information Science Intelligent Systems Engineering Lab.
University of Otago University of Ulster, Magee College
Dunedin, New Zealand Derry, United Kingdom
UzayKaymak T. Martin McGinnity
Dept of Electrical Engineering Intelligent Systems Engineering Lab.
Delft University of Technology University of Ulster, Magee College
Delft, the Netherlands Derry, United Kingdom