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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 control

This 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.

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Over the next decade, the fusion of soft and hard computing will play an increasingly important role in the development of intelligent systems for aerospace, electric power generation, and other safety-critical applications. Computationally Intelligent Hybrid Systems is the only book to examine the practical issues involved in the creation of high-performance, cost-effective applications using a synthesis of neural networks, fuzzy systems, and evolutionary computation with traditional computing methods.

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 control

This is an essential guide for practicing engineers, researchers, and R&D managers who wish to create or understand computationally intelligent hybrid systems, as well as an excellent primary source for graduate courses in soft computing, engineering applications of artificial intelligence, and related topics.

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Contributors xv  
Foreword xvii
 David B. Fogel
 
Preface xix
 
Editor's Introduction to Chapter 1 1
 
1 INTRODUCTION TO FUSION OF SOFT COMPUTING AND HARD COMPUTING 5
 Seppo J. Ovaska
 1.1 Introduction 5
 1.2 Structural Categories 9
 1.3 Characteristic Features 19
 1.4 Characterization of Hybrid Applications 24
 1.5 Conclusions and Discussion 25
 Editor's Introduction to Chapter 2 31
 
2 GENERAL MODEL FOR LARGE-SCALE PLANT APPLICATION 35
 Akimoto Kamiya
 2.1 Introduction 35
 2.2 Control System Architecture 36
 2.3 Forecasting of Market Demand 37
 2.4 Scheduling of Processes 39
 2.5 Supervisory Control 45
 2.6 Local Control 47
 2.7 General Fusion Model and Fusion Categories 49
 2.8 Conclusions 51
 Editor's Introduction to Chapter 3 57
 
3 ADAPTIVE FLIGHT CONTROL: SOFT COMPUTING WITH HARD CONSTRAINTS 61
 Richard E. Saeks
 3.1 Introduction 61
 3.2 The Adaptive Control Algorithms 62
 3.3 Flight Control 67
 3.4 X-43A-LS Autolander 68
 3.5 LOFLYTEw Optimal Control 73
 3.6 LOFLYTEw Stability Augmentation 76
 3.7 Design for Uncertainty with Hard Constraints 82
 3.8 Fusion of Soft Computing and Hard Computing 85
 3.9 Conclusions 85
 Editor's Introduction to Chapter 4 89
 
4 SENSORLESS CONTROL OF SWITCHED RELUCTANCE MOTORS 93
 Adrian David Cheok
 4.1 Introduction 93
 4.2 Fuzzy Logic Model 95
 4.3 Accuracy Enhancement Algorithms 101
 4.4 Simulation Algorithm and Results 108
 4.5 Hardware and Software Implementation 109
 4.6 Experimental Results 111
 4.7 Fusion of Soft Computing and Hard Computing 119
 4.8 Conclusion and Discussion 122
 Editor's Introduction to Chapter 5 125
 
5 ESTIMATION OF UNCERTAINTY BOUNDS FOR LINEAR AND NONLINEAR ROBUST CONTROL 129
 Gregory D. Buckner
 5.1 Introduction 129
 5.2 Robust Control of Active Magnetic Bearings 130
 5.3 Nominal H1 Control of the AMB Test Rig 133
 5.4 Estimating Modeling Uncertainty for H1 Control of the AMB Test Rig 138
 5.5 Nonlinear Robust Control of the AMB Test Rig 148
 5.6 Estimating Model Uncertainty for SMC of the AMB Test Rig 151
 5.7 Fusion of Soft Computing and Hard Computing 159
 5.8 Conclusion 162
 Editor's Introduction to Chapter 6 165
 
6 INDIRECT ON-LINE TOOL WEAR MONITORING 169
 Bernhard Sick
 6.1 Introduction 169
 6.2 Problem Description and Monitoring Architecture 172
 6.3 State of the Art 176
 6.4 New Solution 184
 6.5 Experimental Results 189
 6.6 Fusion of Soft Computing and Hard Computing 192
 6.7 Summary and Conclusions 194
 Editor's Introduction to Chapter 7 199
 
7 PREDICTIVE FILTERING METHODS FOR POWER SYSTEMS APPLICATIONS 203
 Seppo J. Ovaska
 7.1 Introduction 203
 7.2 Multiplicative General-Parameter Filtering 205
 7.3 Genetic Algorithm for Optimizing Filter Tap Cross-Connections 207
 7.4 Design of Multiplierless Basis Filters by Evolutionary Programming 211
 7.5 Predictive Filters for Zero-Crossings Detector 213
 7.6 Predictive Filters for Current Reference Generators 223
 7.7 Fusion of Soft Computing and Hard Computing 233
 7.8 Conclusion 234
 Appendix 7.1: Coefficients of 50-Hz Sinusoid-Predictive FIR Filters 239
 Editor's Introduction to Chapter 8 241
 
8 INTRUSION DETECTION FOR COMPUTER SECURITY 245
 Sung-Bae Cho and Sang-Jun Han
 8.1 Introduction 245
 8.2 Related Works 247
 8.3 Intrusion Detection with Hybrid Techniques 253
 8.4 Experimental Results 261
 8.5 Fusion of Soft Computing and Hard Computing 267
 8.6 Concluding Remarks 268
 Editor's Introduction to Chapter 9 273
 
9 EMOTION GENERATING METHOD ON HUMAN–COMPUTER INTERFACES 277
 Kazuya Mera and Takumi Ichimura
 9.1 Introduction 277
 9.2 Emotion Generating Calculations Method 279
 9.3 Emotion-Oriented Interaction Systems 298
 9.4 Applications of Emotion-Oriented Interaction Systems 302
 9.5 Fusion of Soft Computing and Hard Computing 308
 9.6 Conclusion 310
 Editor's Introduction to Chapter 10 313
 
10 INTRODUCTION TO SCIENTIFIC DATA MINING: DIRECT KERNEL METHODS AND APPLICATIONS 317
 Mark J. Embrechts, Boleslaw Szymanski, and Karsten Sternickel
 10.1 Introduction 317
 10.2 What Is Data Mining? 318
 10.3 Basic Definitions for Data Mining 323
 10.4 Introduction to Direct Kernel Methods 335
 10.5 Direct Kernel Ridge Regression 342
 10.6 Case Study #1: Predicting the Binding Energy for Amino Acids 344
 10.7 Case Study #2: Predicting the Region of Origin for Italian Olive Oils 346
 10.8 Case Study #3: Predicting Ischemia from Magnetocardiography 350
 10.9 Fusion of Soft Computing and Hard Computing 359
 10.10 Conclusions 359
 Editor's Introduction to Chapter 11 363
 
11 WORLD WIDE WEB USAGE MINING 367
 Ajith Abraham
 11.1 Introduction 367
 11.2 Daily and Hourly Web Usage Clustering 372
 11.3 Daily and Hourly Web Usage Analysis 378
 11.3.1 Linear Genetic Programming 379
 11.4 Fusion of Soft Computing and Hard Computing 389
 11.5 Conclusions 393
 References 394
 
INDEX 397
 
ABOUT THE EDITOR 409

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