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

Copyright ~ 2001 by Springer Science+Business Media New York Originally published by Kluwer Academic Publishers in 2001 Softcover reprint ofthe hardcover 1st edition 2001

2001029127

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, photo­copying, 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

Chapter 3. Commercial coin recognisers using neural and fuzzy techniques JM. Moreno, J. Madrenas, and J Cabestany

vii

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 ..................................................................... 1 07

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

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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

Chapter 10. A hybrid approach to breast cancer diagnosis M Sordo, H Buxton, D. Watson

xi

1 Introduction ............................................................................... 300 2 KBANNs .................................................................................... 301

2.1 KBANN methodology .......................................................... 303 2.1.1 "Rules-to-network" .......................................................... 303 2.1.2 Empirical module ............................................................. 305 2.1.3 Overview ofKBANN features ......................................... 305

3 Metabolic features of cancerous breast tissues .......................... 306 4 Knowledge elicitation and refinement.. ..................................... 309 5 31 P MRS data ............................................................................ 311 6 KBANN topology ...................................................................... 313 7 Results ....................................................................................... 314

7 .1 Knowledge-free networks ..................................................... 314 7.2 KBANNs ............................................................................... 314 7.3 Discussion ............................................................................. 318 7.4 Analysis of final connection weights .................................... 321

8 Conclusions ............................................................................... 324 Acknowledgements ................................................................... 326 References ................................................................................. 326

Chapter 11. Artificial neural networks as a computer aid for lung disease detection and classification in ventilation-perfusion lung scans G.D. Tourassi, E.D. Frederick, and R.E. Coleman

1 Introduction ............................................................................... 331 2 Artificial neural networks .......................................................... 333 3 Materials and methods ............................................................... 335

3.1 Overview of the AI diagnostic scheme ................................. 335 3.2 Patient data ............................................................................ 336 3.3 Multifractal texture analysis ................................................. 339 3.4 Artificial neural network predictions .................................... 340 3.5 Performance evaluation ........................................................ 341

4 Results ....................................................................................... 342 4.1 Analysis of perfusion lung scans .......................................... 342

4.1.1 Detection oflung disease ................................................. 342 4.1.2 Detection of pulmonary embolism ................................... 343

xii

4.1.3 Classification oflung diseases ......................................... 345 4.2 Analysis of perfusion-ventilation lung scans ........................ 346

4.2.1 Detection of pulmonary embolism ................................... 346 4.2.2 Classification oflung diseases ......................................... 347

5 Discussion .................................................................................. 348 Acknowledgments ..................................................................... 349 References ................................................................................. 350

Chapter 12. Neural network for classification of focal liver lesions in ultrasound images H. Yoshida

1 Introduction ............................................................................... 355 2 Texture analysis of focal liver lesions by neural networks ....... 358

2.1 Wavelet packets .................................................................... 359 2.2 Multiscale texture features .................................................... 360 2.3 Optimal selection of multiscale texture features .................. 361 2.4 Combination of multiscale features by neural network ........ 363

3 Experimental results .................................................................. 365 3.1 Database of focal liver lesions .............................................. 365 3.2 Experimental conditions ....................................................... 366 3.3 Distribution of feature values ............................................... 367 3.4 Classification performance ................................................... 369

3.4.1 Performance of optimally selected multiscale features .... 369 3.4.2 Performance of wavelet texture features and single-scale

texture features ................................................................. 370 3.4.3 Efficiency of multi scale texture features ......................... 370 3.4.4 Performance of multi scale texture features in the

distinction between different types of lesions .................. 371 4 Discussion .................................................................................. 3 73 5 Conclusions ............................................................................... 374

Acknowledgments ..................................................................... 374 Appendix ................................................................................... 375

Entropy ................................................................................. 375 Root mean square (RMS) variation ...................................... 375 First moment of power spectrum .......................................... 375

References ................................................................................. 376

Index ................................................................................................. 379

Preface

Computational intelligence paradigms have attracted the growing interest of researchers, scientists, engineers and application engineers in a number of everyday applications. These applications are not limited to any particular field and include engineering, business, banking and consumer electronics.

Computational intelligence paradigms include artificial intelligence, artificial neural networks, fuzzy systems and evolutionary computing. Artificial neural networks can mimic the biological information processing mechanism in a very limited sense. Evolutionary computing algorithms are used for optimisation applications, and fuzzy logic provides a basis for representing uncertain and imprecise knowledge. It is not the question of using these techniques but people will wonder in the next century, how we lived without these techniques in the last century.

This book contains twelve chapters. The first chapter, by Konar and Jain, is an introduction to computational intelligence paradigms. In the second chapter, Magnenat-Thalmann, Joslin, and Berner present the networked virtual park. It is shown that the network virtual environments (NVEs) hold the key to interactivity in virtual worlds. This virtual park is more than just a park with trees and flowers as it contains virtual attractions. This chapter shows the construction of the scenes and attractions in the park. It also describes the software that permits the user to interact and watch the attraction along with other users.

The third chapter, by Moreno, Madrenas, and Cabestany, is on commercial coin recognisers using neural and fuzzy techniques. This chapter describes the implementation of a classification/decision engine for an automatic coin recogniser. It is shown that the use of artificial neural and fuzzy models overcome some of the problems encountered when using traditional techniques.

The fourth chapter, by Mraz, Zimic, Lapanja, Virant, and Skrt, is on fuzzy techniques in intelligent household applications. This chapter

xiv

includes several concepts of the use of fuzzy techniques in the analysis and design of control systems for household appliances manufactured by Gorenje GA, ~lovenia. It is demonstrated that the application of intelligent techniques has resulted in energy saving while the production cost remains unaltered.

The fifth chapter, by Edwards, Papadopoulos, and Murray is on the application of neural networks in the prediction of paper curl, an important quality metric in the papermaking industry. It is shown that paper curl can be predicted from parameters defining the current characteristics of a reel of paper and the plant machinery using neural network techniques.

The sixth chapter, by Potter, Bi, Twardus, Thistle, Twery, Ghent, and Teske, is on handling the back calculation problem in aerial spray models using the genetic algorithms. The detailed background discussions of aerial spray practice and simulation, and review of the fundamentals of genetic algorithms are presented in this chapter. The experimental results and future directions are included.

The seventh chapter, by Wilson, Karr, and Messimer, is on genetic algorithm optimization of assembly lines to model filament winding using the witness simulation environment. It is demonstrated that the genetic algorithm serves as an effective optimization method for this purpose, and that it is robust enough to be used with a popular simulation environment.

In the eight chapter, Mehrotra, Karr, and Zeiler present a genetic algorithm (GA) for optimiZing the gust loads for predicting aircraft loads and dynamic response. The effectiveness of the GA-based search is demonstrated via its application to both linear and nonlinear aircraft models, and by considering several different types of loading in the objective function.

The ninth chapter, by Chin and Mills, is on a stochastic dynamic programming technique for property market timing. The development of a market timing strategy for a property investor who has to decide the allocation of investment funds between the risk-free savings deposit and the comparatively risky property investment is presented. It is

xv

demonstrated that the proposed market timing strategy is capable of achieving superior investment returns in the Singapore property market.

The tenth chapter, by Sordo, Buxton, and Watson, is on a hybrid approach to breast cancer diagnosis. A hybrid methodology is proposed that combines knowledge from a domain in the form of simple rules with connectionist learning. This combination allows the use of small sets of data to train the network. It is demonstrated that the proposed approach is capable of classifying complex and limited data in a medical domain.

The eleventh chapter, by Tourassi, Frederick, and Coleman, is on the application of artificial neural networks for the detection and classification of lung disease. The development of a new approach for the diagnostic interpretation of ventilation-perfusion lung scans for patients with clinical suspicion of acute pulmonary embolism.

The last chapter, by Yoshida, is on neural network for classification of focal liver lesions in ultrasound images. The method is unique in the sense that it integrates a process of selection of multi scale texture features and a process of classification by neural network for effective classification. It is demonstrated that the proposed technique has the potential to increase the accuracy of diagnosis of focal liver lesions in ultrasound images.

This book will be useful to researchers, practicing engineers/scientists, and students, who are interested to develop practical applications in computational intelligence environment.

We would like to express our sincere thanks to Berend Jan van der Zwaag, Shaheed Mehta, Ashlesha Jain, and Ajita Jain for their help in the preparation of the manuscript. We are grateful to the authors for their contributions. We also thank the reviewers and Dr Neil Allen for their expertise and time. Our thanks are due to Kluwer Academic Publishers for their excellent editorial assistance.