Computationally intelligent hybrid systems : the fusion of soft computing and hard computing / Edited by Seppo J. Ovaska.

Format:

Book

Contributor:

Ovaska, Seppo J., 1956-

Institute of Electrical and Electronics Engineers.

Series:

IEEE series on computational intelligence

IEEE series on computational intelligence.

Language:

English

Subjects (All):

Intelligent control systems.

Computational intelligence.

Soft computing.

Physical Description:

xxiii, 410 pages : illustrations ; 25 cm.

Place of Publication:

Hoboken, NJ : Wiley, [2005]

Summary:

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.

Contents:

1.1.1 Soft Computing 5

1.1.2 Fusion of Soft-Computing and Hard-Computing Methodologies 7

1.2 Structural Categories 9

1.2.1 Soft Computing and Hard Computing Are Isolated from Each Other 10

1.2.2 Soft Computing and Hard Computing Are Connected in Parallel 11

1.2.3 Soft Computing with Hard-Computing Feedback and Hard Computing with Soft-Computing Feedback 12

1.2.4 Soft Computing Is Cascaded with Hard Computing or Hard Computing Is Cascaded with Soft Computing 12

1.2.5 Soft-Computing-Designed Hard Computing and Hard-Computing-Designed Soft Computing 13

1.2.6 Hard-Computing-Augmented Soft Computing and Soft-Computing-Augmented Hard Computing 14

1.2.7 Hard-Computing-Assisted Soft Computing and Soft-Computing-Assisted Hard Computing 15

1.2.8 Supplementary Categories 16

1.2.9 General Soft-Computing and Hard-Computing Mapping Functions 19

1.3 Characteristic Features 19

1.3.1 Proportional Integral Derivative Controllers 20

1.3.2 Physical Models 20

1.3.3 Optimization Utilizing Local Information 21

1.3.4 General Parameter Adaptation Algorithm 22

1.3.5 Stochastic System Simulators 22

1.3.6 Discussion and Extended Fusion Schemes 22

1.4 Characterization of Hybrid Applications 24

2 General Model for Large-Scale Plant Application / Akimoto Kamiya 35

2.2 Control System Architecture 36

2.3 Forecasting of Market Demand 37

2.4 Scheduling of Processes 39

2.4.1 Problem Decomposition 39

2.4.2 Hybrid Genetic Algorithms 42

2.4.3 Multiobjective Optimization 43

2.5 Supervisory Control 45

2.6 Local Control 47

2.7 General Fusion Model and Fusion Categories 49

3 Adaptive Flight Control: Soft Computing with Hard Constraints / Richard E. Saeks 61

3.2 The Adaptive Control Algorithms 62

3.2.1 Adaptive Dynamic Programming 63

3.2.2 Neural Adaptive Control 64

3.3 Flight Control 67

3.4 X-43A-LS Autolander 68

3.5 LoFLYTE Optimal Control 73

3.6 LoFLYTE Stability Augmentation 76

3.7 Design for Uncertainty with Hard Constraints 82

3.8 Fusion of Soft Computing and Hard Computing 85

4 Sensorless Control of Switched Reluctance Motors / Adrian David Cheok 93

4.2 Fuzzy Logic Model 95

4.2.1 Measurement of Flux Linkage Characteristics 95

4.2.2 Training and Validation of Fuzzy Model 97

4.3 Accuracy Enhancement Algorithms 101

4.3.1 Soft-Computing-Based Optimal Phase Selection 102

4.3.2 Hard-Computing-Based On-Line Resistance Estimation 104

4.3.3 Polynomial Predictive Filtering 105

4.4 Simulation Algorithm and Results 108

4.5 Hardware and Software Implementation 109

4.5.1 Hardware Configuration 109

4.5.2 Software Implementation 110

4.6 Experimental Results 111

4.6.1 Acceleration from Zero Speed 112

4.6.2 Low-Current Low-Speed Test 113

4.6.3 High-Speed Test 114

4.6.4 Test of Step Change of Load 118

4.7 Fusion of Soft Computing and Hard Computing 119

5 Estimation of Uncertainty Bounds for Linear and Nonlinear Robust Control / Gregory D. Buckner 129

5.2 Robust Control of Active Magnetic Bearings 130

5.2.1 Active Magnetic Bearing Test Rig 132

5.3 Nominal H[subscript infinity] Control of the AMB Test Rig 133

5.3.1 Parametric System Identification 133

5.3.2 Uncertainty Bound Specification 135

5.3.3 Nominal H[subscript infinity] Control: Experimental Results 137

5.4 Estimating Modeling Uncertainty for H[subscript infinity] Control of the AMB Test Rig 138

5.4.1 Model Error Modeling 140

5.4.2 Intelligent Model Error Identification 141

5.4.3 Uncertainty Bound Specification 146

5.4.4 Identified H[subscript infinity] Control: Experimental Results 147

5.5 Nonlinear Robust Control of the AMB Test Rig 148

5.5.1 Nominal Sliding Mode Control of the AMB Test Rig 148

5.5.2 Nominal SMC: Experimental Results 150

5.6 Estimating Model Uncertainty for SMC of the AMB Test Rig 151

5.6.1 Intelligent System Identification 151

5.6.2 Intelligent Model Error Identification 155

5.6.3 Intelligent SMC: Experimental Results 156

5.7 Fusion of Soft Computing and Hard Computing 159

6 Indirect On-Line Tool Wear Monitoring / Bernhard Sick 169

6.2 Problem Description and Monitoring Architecture 172

6.3 State of the Art 176

6.3.1 Monitoring Techniques Based on Analytical Models 176

6.3.2 Monitoring Techniques Based on Neural Networks 178

6.3.3 Monitoring Techniques Based on Fusion of Physical and Neural Network Models 181

6.4 New Solution 184

6.4.2 Physical Force Model at Digital Preprocessing Level 185

6.4.3 Dynamic Neural Network at Wear Model Level 187

6.5 Experimental Results 189

6.6 Fusion of Soft Computing and Hard Computing 192

7 Predictive Filtering Methods for Power Systems Applications / Seppo J. Ovaska 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

7.5.1 Single 60-Hz Sinusoid Corrupted by Noise 213

7.5.2 Sequence of 49-, 50-, and 51-Hz Sinusoids Corrupted by Noise 217

7.5.3 Discussion of Zero-Crossings Detection Application 222

7.6 Predictive Filters for Current Reference Generators 223

7.6.1 Sequence of 49-, 50-, and 51-Hz Noisy Sinusoids 225

7.6.2 Sequence of 49-, 50-, and 51-Hz Noisy Sinusoids Corrupted by Harmonics 229

7.6.3 Artificial Current Signal Corrupted by Odd Harmonics 230

7.6.4 Discussion of Current Reference Generation Application 232

7.7 Fusion of Soft Computing and Hard Computing 233

Appendix 7.1 Coefficients of 50-Hz Sinusoid-Predictive FIR Filters 239

8 Intrusion Detection for Computer Security / Sung-Bae Cho, Sang-Jun Han 245

8.2.1 Neural Computing 248

8.2.2 Genetic Computing 249

8.2.3 Fuzzy Logic 251

8.2.4 Probabilistic Reasoning 253

8.3 Intrusion Detection with Hybrid Techniques 253

8.3.2 Preprocessing with Self-Organizing Map 254

8.3.3 Behavior Modeling with Hidden Markov Models 256

8.3.4 Multiple Models Fusion by Fuzzy Logic 259

8.4 Experimental Results 261

8.4.1 Preprocessing 261

8.4.2 Modeling and Intrusion Detection 263

8.5 Fusion of Soft Computing and Hard Computing 267

9 Emotion Generating Method on Human-Computer Interfaces / Kazuya Mera, Takumi Ichimura 277

9.2 Emotion Generating Calculations Method 279

9.2.1 Favorite Value Database 280

9.2.2 Calculation Pleasure/Displeasure for an Event 282

9.2.3 Favorite Value of Modified Element 284

9.2.4 Experimental Result 285

9.2.5 Complicated Emotion Allocating Method 286

9.2.6 Dependency Among Emotion Groups 294

9.2.7 Example of Complicated Emotion Allocating Method 296

9.2.8 Experimental Results 297

9.3 Emotion-Oriented Interaction Systems 298

9.3.1 Facial Expression Generating Method by Neural Network 298

9.3.2 Assign Rules to the Facial Expressions 301

9.4 Applications of Emotion-Oriented Interaction Systems 302

9.4.1 JavaFaceMail 302

9.4.2 JavaFaceChat 307

9.5 Fusion of Soft Computing and Hard Computing 308

10.2.2 Scientific Data Mining 320

10.2.3 The Data Mining Process 321

10.2.4 Data Mining Methods and Techniques 322

10.3 Basic Definitions for Data Mining 323

10.3.1 The MetaNeural Data Format 323

10.3.2 The "Standard Data Mining Problem" 326

10.3.3 Predictive Data Mining 329

10.3.4 Metrics for Assessing Model Quality 333

10.4.1 Data Mining and Machine Learning Dilemmas for Real-World Data 335

10.4.2 Regression Models Based on the Data Kernel 338

10.4.3 Kernel Transformations 339

10.4.4 Dealing with Bias: Centering the Kernel 340

10.5 Direct Kernel Ridge Regression 342

10.5.2 Choosing the Ridge Parameter 343

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

10.8.2 Data Acquisition and Preprocessing 351

10.8.3 Predictive Modeling for Binary Classification of Magnetocardiograms 351

10.8.4 Feature Selection 358

10.9 Fusion of Soft Computing and Hard Computing 359

11 World Wide Web Usage Mining / Ajith Abraham 367

11.2 Daily and Hourly Web Usage Clustering 372

11.2.1 Ant Colony Optimization 372

11.2.2 Fuzzy Clustering Algorithm 374

11.2.3 Self-Organizing Map 376

11.2.4 Analysis of Web Data Clusters 377

11.3 Daily and Hourly Web Usage Analysis 378

11.3.1 Linear Genetic Programming 379

11.3.2 Fuzzy Inference Systems 382

11.3.3 Experimentation Setup, Training, and Performance Evaluation 387

11.4 Fusion of Soft Computing and Hard Computing 389.

Notes:

Includes bibliographical references and index.

ISBN:

0471476684

OCLC:

56953943