Computationally Intelligent Hybrid Systems The Fusion of Soft Computing and Hard Computing

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ISBN: 9780471476689 | 0471476684
Cover: Hardcover
Copyright: 11/4/2004

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This uniquely crafted work combines the experience of many internationally recognized experts in the soft- and hard-computing research worlds to present practicing engineers with the broadest possible array of methodologies for developing innovative and competitive solutions to real-world problems. Each of the chapters illustrates the wide-ranging applicability of the fusion concept in such critical areas as Computer security and data mining Electrical power systems and large-scale plants Motor drives and tool wear monitoring User interfaces and the World Wide Web Aerospace and robust controlThis must-have guide for practicing engineers, researchers, and R&D managers who wish to create or understand computationally intelligent hybrid systems is also an excellent primary source for graduate courses in soft computing, engineering applications of artificial intelligence, and related topics.

Contributors

Foreword

xvii

David B. Fogel

Preface

xix

Editor's Introduction to Chapter

(30)

1 INTRODUCTION TO FUSION OF SOFT COMPUTING AND HARD COMPUTING

(26)

Seppo J. Ovaska

1.1 Introduction

(4)

1.1.1 Soft Computing

(2)

1.1.2 Fusion of Soft-Computing and Hard-Computing Methodologies

(2)

1.2 Structural Categories

(10)

1.2.1 Soft Computing and Hard Computing Are Isolated from Each Other

(1)

1.2.2 Soft Computing and Hard Computing Are Connected in Parallel

(1)

1.2.3 Soft Computing with Hard-Computing Feedback and Hard Computing with Soft-Computing Feedback

(1)

1.2.4 Soft Computing Is Cascaded with Hard Computing or Hard Computing Is Cascaded with Soft Computing

(1)

1.2.5 Soft-Computing-Designed Hard Computing and Hard-Computing-Designed Soft Computing

(1)

1.2.6 Hard-Computing-Augmented Soft Computing and Soft-Computing-Augmented Hard Computing

(1)

1.2.7 Hard-Computing-Assisted Soft Computing and Soft-Computing-Assisted Hard Computing

(1)

1.2.8 Supplementary Categories

(3)

1.2.9 General Soft-Computing and Hard-Computing Mapping Functions

(1)

1.3 Characteristic Features

(5)

1.3.1 Proportional Integral Derivative Controllers

(1)

1.3.2 Physical Models

(1)

1.3.3 Optimization Utilizing Local Information

(1)

1.3.4 General Parameter Adaptation Algorithm

(1)

1.3.5 Stochastic System Simulators

(1)

1.3.6 Discussion and Extended Fusion Schemes

(2)

1.4 Characterization of Hybrid Applications

(1)

1.5 Conclusions and Discussion

(2)

References

(4)

Editor's Introduction to Chapter 2

(26)

2 GENERAL MODEL FOR LARGE-SCALE PLANT APPLICATION

(22)

Akimoto Kamiya

2.1 Introduction

(1)

2.2 Control System Architecture

(1)

2.3 Forecasting of Market Demand

(2)

2.4 Scheduling of Processes

(6)

2.4.1 Problem Decomposition

(3)

2.4.2 Hybrid Genetic Algorithms

(1)

2.4.3 Multiobjective Optimization

(2)

2.5 Supervisory Control

(2)

2.6 Local Control

(2)

2.7 General Fusion Model and Fusion Categories

(2)

2.8 Conclusions

(1)

References

(6)

Editor's Introduction to Chapter 3

(32)

3 ADAPTIVE FLIGHT CONTROL: SOFT COMPUTING WITH HARD CONSTRAINTS

(28)

Richard E. Saeks

3.1 Introduction

(1)

3.2 The Adaptive Control Algorithms

(5)

3.2.1 Adaptive Dynamic Programming

(1)

3.2.2 Neural Adaptive Control

(3)

3.3 Flight Control

(1)

3.4 X-43A-LS Autolander

(5)

3.5 LoFLYTE® Optimal Control

(3)

3.6 LoFLYTE® Stability Augmentation

(6)

3.7 Design for Uncertainty with Hard Constraints

(3)

3.8 Fusion of Soft Computing and Hard Computing

(1)

3.9 Conclusions

(1)

References

(3)

Editor's Introduction to Chapter 4

(36)

4 SENSORLESS CONTROL OF SWITCHED RELUCTANCE MOTORS

(32)

Adrian David Cheok

4.1 Introduction

(2)

4.2 Fuzzy Logic Model

(6)

4.2.1 Measurement of Flux Linkage Characteristics

(2)

4.2.2 Training and Validation of Fuzzy Model

(4)

4.3 Accuracy Enhancement Algorithms

101

(7)

4.3.1 Soft-Computing-Based Optimal Phase Selection

102

(2)

4.3.2 Hard-Computing-Based On-Line Resistance Estimation

104

(1)

4.3.3 Polynomial Predictive Filtering

105

(3)

4.4 Simulation Algorithm and Results

108

(1)

4.5 Hardware and Software Implementation

109

(2)

4.5.1 Hardware Configuration

109

(1)

4.5.2 Software Implementation

110

(1)

4.6 Experimental Results

111

(8)

4.6.1 Acceleration from Zero Speed

112

(1)

4.6.2 Low-Current Low-Speed Test

113

(1)

4.6.3 High-Speed Test

114

(4)

4.6.4 Test of Step Change of Load

118

(1)

4.7 Fusion of Soft Computing and Hard Computing

119

(3)

4.8 Conclusion and Discussion

122

(1)

References

122

(3)

Editor's Introduction to Chapter 5

125

(40)

5 ESTIMATION OF UNCERTAINTY BOUNDS FOR LINEAR AND NONLINEAR ROBUST CONTROL

129

(36)

Gregory D. Buckner

5.1 Introduction

129

(1)

5.2 Robust Control of Active Magnetic Bearings

130

(3)

5.2.1 Active Magnetic Bearing Test Rig

132

(1)

5.3 Nominal H~ Control of the AMB Test Rig

133

(5)

5.3.1 Parametric System Identification

133

(2)

5.3.2 Uncertainty Bound Specification

135

(2)

5.3.3 Nominal Ho. Control: Experimental Results

137

(1)

5.4 Estimating Modeling Uncertainty for He. Control of the AMB Test Rig

138

(10)

5.4.1 Model Error Modeling

140

(1)

5.4.2 Intelligent Model Error Identification

141

(5)

5.4.3 Uncertainty Bound Specification

146

(1)

5.4.4 Identified H~ Control: Experimental Results

147

(1)

5.5 Nonlinear Robust Control of the AMB Test Rig

148

(3)

5.5.1 Nominal Sliding Mode Control of the AMB Test Rig

148

(2)

5.5.2 Nominal SMC: Experimental Results

150

(1)

5.6 Estimating Model Uncertainty for SMC of the AMB Test Rig

151

(8)

5.6.1 Intelligent System Identification

151

(4)

5.6.2 Intelligent Model Error Identification

155

(1)

5.6.3 Intelligent SMC: Experimental Results

156

(3)

5.7 Fusion of Soft Computing and Hard Computing

159

(3)

5.8 Conclusion

162

(1)

References

162

(3)

Editor's Introduction to Chapter 6

165

(34)

6 INDIRECT ON-LINE TOOL WEAR MONITORING

169

(30)

Bernhard Sick

6.1 Introduction

169

(3)

6.2 Problem Description and Monitoring Architecture

172

(4)

6.3 State of the Art

176

(8)

6.3.1 Monitoring Techniques Based on Analytical Models

176

(2)

6.3.2 Monitoring Techniques Based on Neural Networks

178

(3)

6.3.3 Monitoring Techniques Based on Fusion of Physical and Neural Network Models

181

(3)

6.4 New Solution

184

(5)

6.4.1 Solution Outline

184

(1)

6.4.2 Physical Force Model at Digital Preprocessing Level

185

(2)

6.4.3 Dynamic Neural Network at Wear Model Level

187

(2)

6.5 Experimental Results

189

(3)

6.6 Fusion of Soft Computing and Hard Computing

192

(2)

6.7 Summary and Conclusions

194

(1)

References

195

(4)

Editor's Introduction to Chapter 7

199

(42)

7 PREDICTIVE FILTERING METHODS FOR POWER SYSTEMS APPLICATIONS

203

(38)

Seppo J. Ovaska

7.1 Introduction

203

(2)

7.2 Multiplicative General-Parameter Filtering

205

(2)

7.3 Genetic Algorithm for Optimizing Filter Tap Cross-Connections

207

(4)

7.4 Design of Multiplierless Basis Filters by Evolutionary Programming

211

(2)

7.5 Predictive Filters for Zero-Crossings Detector

213

(10)

7.5.1 Single 60-Hz Sinusoid Corrupted by Noise

213

(4)

7.5.2 Sequence of 49-, 50-, and 51-Hz Sinusoids Corrupted by Noise

217

(5)

7.5.3 Discussion of Zero-Crossings Detection Application

222

(1)

7.6 Predictive Filters for Current Reference Generators

223

(10)

7.6.1 Sequence of 49-, 50-, and 51-Hz Noisy Sinusoids

225

(4)

7.6.2 Sequence of 49-, 50-, and 51-Hz Noisy Sinusoids Corrupted by Harmonics

229

(1)

7.6.3 Artificial Current Signal Corrupted by Odd Harmonics

230

(2)

7.6.4 Discussion of Current Reference Generation Application

232

(1)

7.7 Fusion of Soft Computing and Hard Computing

233

(1)

7.8 Conclusion

234

(3)

References

237

(2)

Appendix 7.1: Coefficients of 50-Hz Sinusoid-Predictive FIR Filters

239

(2)

Editor's Introduction to Chapter 8

241

(32)

8 INTRUSION DETECTION FOR COMPUTER SECURITY

245

(28)

Sung-Bae Cho and Sang-Jun Han

8.1 Introduction

245

(2)

8.2 Related Works

247

(6)

8.2.1 Neural Computing

248

(1)

8.2.2 Genetic Computing

249

(2)

8.2.3 Fuzzy Logic

251

(2)

8.2.4 Probabilistic Reasoning

253

(1)

8.3 Intrusion Detection with Hybrid Techniques

253

(8)

8.3.1 Overview

254

(1)

8.3.2 Preprocessing with Self-Organizing Map

254

(2)

8.3.3 Behavior Modeling with Hidden Markov Models

256

(3)

8.3.4 Multiple Models Fusion by Fuzzy Logic

259

(2)

8.4 Experimental Results

261

(6)

8.4.1 Preprocessing

261

(2)

8.4.2 Modeling and Intrusion Detection

263

(4)

8.5 Fusion of Soft Computing and Hard Computing

267

(1)

8.6 Concluding Remarks

268

(2)

References

270

(3)

Editor's Introduction to Chapter 9

273

(40)

9 EMOTION GENERATING METHOD ON HUMAN-COMPUTER INTERFACES

277

(36)

Kazuya Mera and Takumi Ichimura

9.1 Introduction

277

(2)

9.2 Emotion Generating Calculations Method

279

(19)

9.2.1 Favorite Value Database

280

(2)

9.2.2 Calculation Pleasure/Displeasure for an Event

282

(2)

9.2.3 Favorite Value of Modified Element

284

(1)

9.2.4 Experimental Result

285

(1)

9.2.5 Complicated Emotion Allocating Method

286

(8)

9.2.6 Dependency Among Emotion Groups

294

(2)

9.2.7 Example of Complicated Emotion Allocating Method

296

(1)

9.2.8 Experimental Results

297

(1)

9.3 Emotion-Oriented Interaction Systems

298

(4)

9.3.1 Facial Expression Generating Method by Neural Network

298

(3)

9.3.2 Assign Rules to the Facial Expressions

301

(1)

9.4 Applications of Emotion-Oriented Interaction Systems

302

(6)

9.4.1 JavaFaceMail

302

(5)

9.4.2 JavaFaceChat

307

(1)

9.5 Fusion of Soft Computing and Hard Computing

308

(2)

9.6 Conclusion

310

(1)

References

311

(2)

Editor's Introduction to Chapter 10

313

(4)

10 INTRODUCTION TO SCIENTIFIC DATA MINING: DIRECT KERNEL METHODS AND APPLICATIONS

317

(46)

Mark J. Embrechts, Boleslaw Szymanski, and Karsten Sternickel

10.1 Introduction

317

(1)

10.2 What Is Data Mining?

318

(49)

10.2.1 Introduction to Data Mining

318

(2)

10.2.2 Scientific Data Mining

320

(1)

10.2.3 The Data Mining Process

321

(1)

10.2.4 Data Mining Methods and Techniques

322

(1)

10.3 Basic Definitions for Data Mining

323

(1)

10.3.1 The MetaNeural Data Format

323

(3)

10.3.2 The "Standard Data Mining Problem"

326

(3)

10.3.3 Predictive Data Mining

329

(4)

10.3.4 Metrics for Assessing Model Quality

333

(2)

10.4 Introduction to Direct Kernel Methods

335

(1)

10.4.1 Data Mining and Machine Learning Dilemmas for Real-World Data

335

(3)

10.4.2 Regression Models Based on the Data Kernel

338

(1)

10.4.3 Kernel Transformations

339

(1)

10.4.4 Dealing with Bias: Centering the Kernel

340

(2)

10.5 Direct Kernel Ridge Regression

342

(1)

10.5.1 Overview

342

(1)

10.5.2 Choosing the Ridge Parameter

343

(1)

10.6 Case Study #1: Predicting the Binding Energy for Amino Acids

344

(2)

10.7 Case Study #2: Predicting the Region of Origin for Italian Olive Oils

346

(4)

10.8 Case Study #3: Predicting Ischemia from Magnetocardiography

350

(1)

10.8.1 Introduction to Magnetocardiography

350

(1)

10.8.2 Data Acquisition and Preprocessing

351

(1)

10.8.3 Predictive Modeling for Binary Classification of Magnetocardiograms

351

(7)

10.8.4 Feature Selection

358

(1)

10.9 Fusion of Soft Computing and Hard Computing

359

(1)

10.10 Conclusions

359

(1)

References

360

(3)

Editor's Introduction to Chapter 11

363

(4)

11 WORLD WIDE WEB USAGE MINING

367

(30)

Ajith Abraham

11.1 Introduction

367

(5)

11.2 Daily and Hourly Web Usage Clustering

372

(6)

11.2.1 Ant Colony Optimization

372

(2)

11.2.2 Fuzzy Clustering Algorithm

374

(2)

11.2.3 Self-Organizing Map

376

(1)

11.2.4 Analysis of Web Data Clusters

377

(1)

11.3 Daily and Hourly Web Usage Analysis

378

(11)

11.3.1 Linear Genetic Programming

379

(3)

11.3.2 Fuzzy Inference Systems

382

(5)

11.3.3 Experimentation Setup, Training, and Performance Evaluation

387

(2)

11.4 Fusion of Soft Computing and Hard Computing

389

(4)

11.5 Conclusions

393

(1)

References

394

(3)

INDEX

397

(12)

ABOUT THE EDITOR

409

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Computationally Intelligent Hybrid Systems The Fusion of Soft Computing and Hard Computing

Summary

Author Biography

Table of Contents

Supplemental Materials

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Computationally Intelligent Hybrid Systems The Fusion of Soft Computing and Hard Computing

Summary

Author Biography

Table of Contents

Supplemental Materials

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