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

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