Computationally Intelligent Hybrid Systems: The Fusion of Soft Computing and Hard Computing / Edition 1

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Overview

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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Editorial Reviews

From the Publisher
"…the only book to examine the practical issue 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." (International Journal of General Systems, June 2005)

"...accessible for practical engineers and at the same time quite interesting for theoretical computer scientists...it will inspire more people to use the (currently under-utilized) fusion techniques." (Journal of Intelligent & Fuzzy Systems, Vol. 16, No. 3, 2005)

"…these well-written papers serve to offer insight into the powerful combination of soft and hard computing that is now being applied…to real-world applications." (Computing Reviews.com, June 10, 2005)

"This is the first book to treat the subject. With this work, the editor hopes to bridge the gap between the proponents of soft computing and hard computing." (E-STREAMS, May 2005)

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Product Details

Meet the Author

SEPPO J. OVASKA, DSc (Tech), is a professor in the Department of Electrical and Communications Engineering at Helsinki University of Technology, Finland. He is a senior member of the IEEE, and has published more than 180 papers in peer-reviewed journals and international conferences.

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Table of Contents

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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