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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.
Foreword xviiDavid B. Fogel
Preface xix
Editor's Introduction to Chapter 1 1
1 INTRODUCTION TO FUSION OF SOFT COMPUTING AND HARD COMPUTING 5Seppo J. Ovaska1.1 Introduction 51.2 Structural Categories 91.3 Characteristic Features 191.4 Characterization of Hybrid Applications 241.5 Conclusions and Discussion 25Editor's Introduction to Chapter 2 31
2 GENERAL MODEL FOR LARGE-SCALE PLANT APPLICATION 35Akimoto Kamiya2.1 Introduction 352.2 Control System Architecture 362.3 Forecasting of Market Demand 372.4 Scheduling of Processes 392.5 Supervisory Control 452.6 Local Control 472.7 General Fusion Model and Fusion Categories 492.8 Conclusions 51Editor's Introduction to Chapter 3 57
3 ADAPTIVE FLIGHT CONTROL: SOFT COMPUTING WITH HARD CONSTRAINTS 61Richard E. Saeks3.1 Introduction 613.2 The Adaptive Control Algorithms 623.3 Flight Control 673.4 X-43A-LS Autolander 683.5 LOFLYTEw Optimal Control 733.6 LOFLYTEw Stability Augmentation 763.7 Design for Uncertainty with Hard Constraints 823.8 Fusion of Soft Computing and Hard Computing 853.9 Conclusions 85Editor's Introduction to Chapter 4 89
4 SENSORLESS CONTROL OF SWITCHED RELUCTANCE MOTORS 93Adrian David Cheok4.1 Introduction 934.2 Fuzzy Logic Model 954.3 Accuracy Enhancement Algorithms 1014.4 Simulation Algorithm and Results 1084.5 Hardware and Software Implementation 1094.6 Experimental Results 1114.7 Fusion of Soft Computing and Hard Computing 1194.8 Conclusion and Discussion 122Editor's Introduction to Chapter 5 125
5 ESTIMATION OF UNCERTAINTY BOUNDS FOR LINEAR AND NONLINEAR ROBUST CONTROL 129Gregory D. Buckner5.1 Introduction 1295.2 Robust Control of Active Magnetic Bearings 1305.3 Nominal H1 Control of the AMB Test Rig 1335.4 Estimating Modeling Uncertainty for H1 Control of the AMB Test Rig 1385.5 Nonlinear Robust Control of the AMB Test Rig 1485.6 Estimating Model Uncertainty for SMC of the AMB Test Rig 1515.7 Fusion of Soft Computing and Hard Computing 1595.8 Conclusion 162Editor's Introduction to Chapter 6 165
6 INDIRECT ON-LINE TOOL WEAR MONITORING 169Bernhard Sick6.1 Introduction 1696.2 Problem Description and Monitoring Architecture 1726.3 State of the Art 1766.4 New Solution 1846.5 Experimental Results 1896.6 Fusion of Soft Computing and Hard Computing 1926.7 Summary and Conclusions 194Editor's Introduction to Chapter 7 199
7 PREDICTIVE FILTERING METHODS FOR POWER SYSTEMS APPLICATIONS 203Seppo J. Ovaska7.1 Introduction 2037.2 Multiplicative General-Parameter Filtering 2057.3 Genetic Algorithm for Optimizing Filter Tap Cross-Connections 2077.4 Design of Multiplierless Basis Filters by Evolutionary Programming 2117.5 Predictive Filters for Zero-Crossings Detector 2137.6 Predictive Filters for Current Reference Generators 2237.7 Fusion of Soft Computing and Hard Computing 2337.8 Conclusion 234Appendix 7.1: Coefficients of 50-Hz Sinusoid-Predictive FIR Filters 239Editor's Introduction to Chapter 8 241
8 INTRUSION DETECTION FOR COMPUTER SECURITY 245Sung-Bae Cho and Sang-Jun Han8.1 Introduction 2458.2 Related Works 2478.3 Intrusion Detection with Hybrid Techniques 2538.4 Experimental Results 2618.5 Fusion of Soft Computing and Hard Computing 2678.6 Concluding Remarks 268Editor's Introduction to Chapter 9 273
9 EMOTION GENERATING METHOD ON HUMAN–COMPUTER INTERFACES 277Kazuya Mera and Takumi Ichimura9.1 Introduction 2779.2 Emotion Generating Calculations Method 2799.3 Emotion-Oriented Interaction Systems 2989.4 Applications of Emotion-Oriented Interaction Systems 3029.5 Fusion of Soft Computing and Hard Computing 3089.6 Conclusion 310Editor's Introduction to Chapter 10 313
10 INTRODUCTION TO SCIENTIFIC DATA MINING: DIRECT KERNEL METHODS AND APPLICATIONS 317Mark J. Embrechts, Boleslaw Szymanski, and Karsten Sternickel10.1 Introduction 31710.2 What Is Data Mining? 31810.3 Basic Definitions for Data Mining 32310.4 Introduction to Direct Kernel Methods 33510.5 Direct Kernel Ridge Regression 34210.6 Case Study #1: Predicting the Binding Energy for Amino Acids 34410.7 Case Study #2: Predicting the Region of Origin for Italian Olive Oils 34610.8 Case Study #3: Predicting Ischemia from Magnetocardiography 35010.9 Fusion of Soft Computing and Hard Computing 35910.10 Conclusions 359Editor's Introduction to Chapter 11 363
11 WORLD WIDE WEB USAGE MINING 367Ajith Abraham11.1 Introduction 36711.2 Daily and Hourly Web Usage Clustering 37211.3 Daily and Hourly Web Usage Analysis 37811.3.1 Linear Genetic Programming 37911.4 Fusion of Soft Computing and Hard Computing 38911.5 Conclusions 393References 394
INDEX 397
ABOUT THE EDITOR 409
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