Practical Applications of Computational Intelligence Techniques

391 Pages·2001·11.804 MB·English

by  A. Konar, L. C. Jain (auth.), Lakhmi Jain, Philippe De Wilde (eds.)

PRACTICAL APPLICATIONS OF
COMPUTATIONAL
INTELLIGENCE TECHNIQUES
INTERNATIONAL SERIES IN
INTELLIGENT TECHNOLOGIES
Prof. Dr. Dr. h.c. Hans-Jiirgen Zimmermann, Editor
European Laboratory for Intelligent
Techniques Engineering
Aachen, Germany
Other books in the series:
Fuzzy Databases: Principles and Applications
by Frederick E. Petry with Patrick Bose
Distributed Fuzzy Control of Multivariable Systems
by Alexander Gegov
Fuzzy Modelling: Paradigms and Practices
by Witold Pedrycz
Fuzzy Logic Foundations and Industrial Applications
by Da Ruan
Fuzzy Sets in Engineering Design and Configuration
by Hans-Juergen Sebastian and Erik K. Antonsson
Consensus Under Fuzziness
by Mario Fedrizzi, Janusz Kacprzyk, and Hannu Nurmi
Uncertainty Analysis in Enginerring Sciences:
Fuzzy Logic, Statistices, and Neural Network Approach
by Bilal M. Ayyub and Madan M. Gupta
Fuzzy Modeling for Control
by Robert Babuska
Traffic Control and Transport Planning:
A Fuzzy Sets and Neural Networks Approach
by Dusan Teodorovic and Katarina VukadinoviC
Fuzzy Algorithms for Control
by H.B. Verbruggen, H.-J.Zimmermann. and R. BabUska
Intelligent Systems and Interfaces
by Horia-Nicolai Teodorescu, Daniel Mlynek, Abraham Kandel
and H.J. Zimmermann
PRACTICAL APPLICATIONS OF
COMPUTATIONAL
INTELLIGENCE TECHNIQUES
Edited by
LakhmiJain
University of South Australia, Adelaide
and
Philippe De Wilde
University of London
~.
"
SPRINGER-SCIENCE+BUSINESS MEDIA, LLC
Library of Congress Cataloging-in-Publication Data
Practical applications of computational intelligence techniques I edited by Lakhmi Jain
and Philippe De Wilde.
p. cm. -- (International series in intelligent technologies ; 16)
IncJudes bibliographical references and index.
ISBN 978-94-010-3868-3 ISBN 978-94-010-0678-1 (eBook)
DOI 10.1007/978-94-010-0678-1
1. Computational intelligence--Industrial applications. I. Jain, L. C. 11. De Wilde,
Philippe, 1958- III. Series.
Q342 .P73 2001
006.3--dc21
2001029127
Copyright ~ 2001 by Springer Science+Business Media New York
Originally published by Kluwer Academic Publishers in 2001
Softcover reprint ofthe hardcover 1st edition 2001
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
Chapter 1.
An introduction to computational intelligence paradigms
A. Konar and L. C. Jain
1 Computational intelligence - a formal definition .......................... 1
2 The logic of fuzzy sets ................................................................... 2
3 Computational models of neural nets .......................................... 13
3.1 The back-propagation learning algorithm .............................. 15
3.2 Hopfield nets ........................................................................... 20
3.2.1 Binary Hopfield net.. .......................................................... 20
3.2.2 Continuous Hopfield net .................................................... 22
3.3 Self-organizing feature map ................................................... 22
3.4 Reinforcement learning .......................................................... 24
3.4.1 Temporal difference leaming ............................................. 26
3.4.2 Active leaming ................................................................... 27
3.4.3 Q-Ieaming .......................................................................... 27
4 Genetic algorithms ....................................................................... 27
4.1 Deterministic explanation of Holland's observation .............. 31
4.2 Stochastic explanation of GA ................................................. 32
4.3 The fundamental theorem of genetic algorithms (schema
theorem) .................................................................................. 32
4.4 The Markov model for convergence analysis ......................... 34
5 Beliefnetworks ............................................................................ 38
6 Computational learning theory ................................................... .45
7 Synergism of the computational intelligence paradigms ............ .47
7.1 Neuro-fuzzy synergism ........................................................... 47
7.1.1 Weakly coupled neuro-fuzzy systems ................................ 48
7.1.2 Tightly coupled neuro-fuzzy systems ................................ 49
7.2 Fuzzy-GA synergism .............................................................. 51
7.3 Neuro-GA synergism .............................................................. 52
7.3.1 Adaptation ofa neural learning algorithm using GA ......... 52
7.4 GA-belief network synergism ................................................. 54
8 Conclusions and future directions ............................................... 55
References ................................................................................... 57
vi
Chapter 2.
Networked virtual park
N. Magnenat-Thalmann, C. Joslin, and U. Berner
1 Introduction ................................................................................. 65
2 The attraction builder .................................................................. 66
2.1 Introduction ............................................................................. 66
2.2 Virtual avatars ......................................................................... 67
2.2.1 Avatar realism .................................................................... 68
2.2.2 Face animation ................................................................... 69
2.2.3 Body animation .................................................................. 70
2.2.4 Speech animation ............................................................... 70
2.3 The scene ................................................................................ 71
2.4 Adding interactivity ................................................................ 71
2.5 Possible attractions ................................................................. 75
3 Networked virtual environment system ....................................... 76
3.1 Introduction ............................................................................. 76
3.2 Overview of system architecture ........................................... 77
3.3 The client ................................................................................ 77
3.3.1 Introduction ........................................................................ 77
3.3.2 System communication ...................................................... 78
3.3.3 Scene management ............................................................. 79
3.3.4 Avatar representation and animation ................................. 80
3.3.5 Navigation .......................................................................... 81
3.3.6 Audio communication ........................................................ 82
3.3.7 Speech ................................................................................ 82
3.3.8 Devices ............................................................................... 82
3.3.9 Network manager ............................................................... 83
4 The server .................................................................................... 84
4.1 Server overview ...................................................................... 84
4.2 Server database ....................................................................... 85
4.3 Client-server communication protocol ................................... 86
5 Conclusion ................................................................................... 86
Acknowledgements ..................................................................... 87
References ................................................................................... 87
vii
Chapter 3.
Commercial coin recognisers using neural and fuzzy techniques
JM. Moreno, J. Madrenas, and J Cabestany
1 Introduction ................................................................................. 89
1.1 Problem statement .................................................................. 90
2 Problem analysis and database compilation ................................ 92
2.1 Problem analysis ..................................................................... 93
2.2 Database compilation .............................................................. 95
2.3 Optical measurements preprocessing ..................................... 96
3 Approach using artificial neural networks models ...................... 98
3.1 Neural model selection ........................................................... 99
3.2 Validation stage for the rejection of outliers ........................ 104
3.3 Implementation ..................................................................... 10 7
4 Approach using fuzzy logic models .......................................... 113
4.1 Fuzzy model selection and experimental results .................. 114
4.2 Implementation ..................................................................... 117
5 Conclusions ............................................................................... 117
References ................................................................................. 119
Chapter 4.
Fuzzy techniques in intelligent household appliances
M Mraz, N Zimic, I. Lapanja, J Virant, and B. Skrt
1 Introduction ............................................................................... 121
2 Fuzzy approaches for intelligent devices .................................. 122
3 Introducing fuzziness to kitchen oven ....................................... 125
3.1 Thermostatically controlled oven ......................................... 126
3.2 Design of a fuzzy controller .................................................. 126
3.3 Results of fuzzy control ........................................................ 130
4 Refrigerator-freezer control using fuzzy logic .......................... 133
4.1 Refrigerating operating regime ............................................. 134
4.2 Fuzzy controller for the refrigerating device ........................ 135
4.3 Results of fuzzy control of refrigerating device ................... 136
4.4 Results of fuzzy control of freezing device .......................... 136
4.5 Measuring entire appliance within standard test
environment .......................................................................... 137
5 Model and simulation of refrigerating-freezing appliance using
one compressor .......................................................................... 138
5.1 Simulation results ................................................................. 139
viii
6 Hardware implementation ......................................................... 141
7 Decrease of energy consumption from national point-of-view.142
8 Conclusion ................................................................................. 143
References ................................................................................. 144
Chapter 5.
Neural prediction in industry: increasing reliability through use of
confidence measures and model combination
P.J. Edwards, G. Papadopoulos, and A.F. Murray
1 Introduction ............................................................................... 147
2 Paper curl prediction .................................................................. 149
2.1 Data collection ...................................................................... 150
3 Neural network model development ......................................... 151
3.1 Preprocessing ........................................................................ 152
3.2 Training ................................................................................. 154
4 Model combination .................................................................... 155
4.1 Cranking ............................................................................... 156
5 Confidence measures ................................................................. 158
6 Results ....................................................................................... 162
6.1 In-specificationlout-of-specification classifier ..................... 162
6.2 Curl prediction ...................................................................... 163
6.3 Model combination ............................................................... 164
6.4 Confidence measures ............................................................ 167
7 Discussion .................................................................................. 169
Acknowledgments ..................................................................... 170
References ................................................................................. 170
Chapter 6.
Handling the back calculation problem in aerial spray models
using a genetic algorithm
W.D. Potter, W. Bi, D. Twardus, H. Thistle, MJ. Twery, J. Ghent, and
M Teske
1 Introduction ............................................................................... 178
2 Early spray models .................................................................... 179
2.1 FSCBG .................................................................................. 179
2.2 AGDISP ................................................................................ 180
2.3 AgDRIFT .............................................................................. 182
2.4 Computer simulation models in common ............................. 183
ix
3 Genetic algorithms ..................................................................... 185
3.1 Main GA components and how the GA works ..................... 185
3.2 Sample GA applications ....................................................... 188
4 Development of Fortran-SAGA ................................................ 188
4.1 AGDISP DOS version 7.0 .................................................... 189
4.2 The Fortran GA ..................................................................... 190
4.3 Preliminary Fortran-based SAGA ........................................ 190
4.4 Results and discussion of Fortran-based SAGA ................... 191
5 Development of VB-SAGA 1.0 ................................................ 196
5.1 VB-SAGA 1.0 ....................................................................... 196
5.2 Exhaustive search test ........................................................... 202
5.2.1 VB-SAGAl.0 test ............................................................ 204
5.3 VB-SAGAl.O experiments and results ................................. 205
6 Development of VB-SAGA 2.0 ................................................ 209
6.1 VB-SAGA2.0 menu items .................................................... 209
6.2 The self-adaptive GA ............................................................ 211
6.2.1 Fuzzy logic controL ......................................................... 212
6.2.2 Development of self-adaptive GA in VB-SAGA2.0 ....... 212
6.3 Results ofVB-SAGA2.0 ...................................................... 215
7 Summary and conclusions ......................................................... 217
References ................................................................................. 219
Chapter 7.
Genetic algorithm optimization of a filament winding process
modeled in WITNESS
E. Wilson, c.L. Karr, and S. Messimer
1 Introduction ............................................................................... 223
2 Filament winding model ............................................................ 225
3 Genetic algorithm interface ....................................................... 229
4 Results ....................................................................................... 233
5 Conclusions ............................................................................... 236
6 Summary .................................................................................... 238
Acknowledgments ..................................................................... 238
References ................................................................................. 238
x
Chapter 8.
Genetic algorithm for optimizing the gust loads for predicting
aircraft loads and dynamic response
R. Mehrotra, C.L. Karr, and T.A. Zeiler
1 Introduction ............................................................................... 241
2 Problem statement and related mathematical underpinnings .... 244
2.1 Statistical discrete gust (SDG) model.. ................................. 245
2.2 Methodology of the search for a worst-case gust ................. 245
2.2.1 Waveform construction .................................................... 246
2.2.2 Modified von Karman gust pre-filter ............................... 248
2.2.3 Aircraft model simulation ................................................ 249
2.3 Linear aircraft model ............................................................ 250
3 An approach using genetic algorithm ........................................ 253
4 Results of approach on linear aircraft model.. ........................... 256
4.1 Wing bending moment ......................................................... 256
4.2 Engine lateral acceleration .................................................... 257
4.3 Wing torque .......................................................................... 260
4.4 Aircraft normal acceleration ................................................. 263
5 Summary and conclusion .......................................................... 265
Acknowledgments ..................................................................... 266
References ................................................................................. 266
Chapter 9.
A stochastic dynamic programming technique for property market
timing
T. C. Chin and G. T Mills
1 Introduction ............................................................................... 269
2 Review of theoretical considerations ......................................... 271
3 Specification of market timing model ....................................... 274
4 Stochastic dynamic programming ............................................. 282
5 Data used in the simulation study .............................................. 286
6 Performance and evaluation tests .............................................. 286
6.1 Performance of market timing strategy ................................ 289
6.2 Comparison for various investment horizons ....................... 290
6.3 Comparison of efficiency ratios ............................................ 292
6.4 Comparison for various transaction expenses ...................... 292
6.5 Comparison for various cash downpayments ....................... 293
7 Conclusions ............................................................................... 295
References ................................................................................. 296