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

by Ovaska, Seppo J.

• ISBN13: 9780471476689
• ISBN10: 0471476684
• Edition: 1st
• Format: Hardcover
• Copyright: 2004-11-04
• Publisher: Wiley-IEEE Press

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 controlThis 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.

Author Biography

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.

Table of Contents

Contributors	xv
Foreword	xvii
David B. Fogel
Preface	xix
Editor's Introduction to Chapter	1	(30)
1 INTRODUCTION TO FUSION OF SOFT COMPUTING AND HARD COMPUTING	5	(26)
Seppo J. Ovaska
1.1 Introduction	5	(4)
1.1.1 Soft Computing	5	(2)
1.1.2 Fusion of Soft-Computing and Hard-Computing Methodologies	7	(2)
1.2 Structural Categories	9	(10)
1.2.1 Soft Computing and Hard Computing Are Isolated from Each Other	10	(1)
1.2.2 Soft Computing and Hard Computing Are Connected in Parallel	11	(1)
1.2.3 Soft Computing with Hard-Computing Feedback and Hard Computing with Soft-Computing Feedback	12	(1)
1.2.4 Soft Computing Is Cascaded with Hard Computing or Hard Computing Is Cascaded with Soft Computing	12	(1)
1.2.5 Soft-Computing-Designed Hard Computing and Hard-Computing-Designed Soft Computing	13	(1)
1.2.6 Hard-Computing-Augmented Soft Computing and Soft-Computing-Augmented Hard Computing	14	(1)
1.2.7 Hard-Computing-Assisted Soft Computing and Soft-Computing-Assisted Hard Computing	15	(1)
1.2.8 Supplementary Categories	16	(3)
1.2.9 General Soft-Computing and Hard-Computing Mapping Functions	19	(1)
1.3 Characteristic Features	19	(5)
1.3.1 Proportional Integral Derivative Controllers	20	(1)
1.3.2 Physical Models	20	(1)
1.3.3 Optimization Utilizing Local Information	21	(1)
1.3.4 General Parameter Adaptation Algorithm	22	(1)
1.3.5 Stochastic System Simulators	22	(1)
1.3.6 Discussion and Extended Fusion Schemes	22	(2)
1.4 Characterization of Hybrid Applications	24	(1)
1.5 Conclusions and Discussion	25	(2)
References	27	(4)
Editor's Introduction to Chapter 2	31	(26)
2 GENERAL MODEL FOR LARGE-SCALE PLANT APPLICATION	35	(22)
Akimoto Kamiya
2.1 Introduction	35	(1)
2.2 Control System Architecture	36	(1)
2.3 Forecasting of Market Demand	37	(2)
2.4 Scheduling of Processes	39	(6)
2.4.1 Problem Decomposition	39	(3)
2.4.2 Hybrid Genetic Algorithms	42	(1)
2.4.3 Multiobjective Optimization	43	(2)
2.5 Supervisory Control	45	(2)
2.6 Local Control	47	(2)
2.7 General Fusion Model and Fusion Categories	49	(2)
2.8 Conclusions	51	(1)
References	51	(6)
Editor's Introduction to Chapter 3	57	(32)
3 ADAPTIVE FLIGHT CONTROL: SOFT COMPUTING WITH HARD CONSTRAINTS	61	(28)
Richard E. Saeks
3.1 Introduction	61	(1)
3.2 The Adaptive Control Algorithms	62	(5)
3.2.1 Adaptive Dynamic Programming	63	(1)
3.2.2 Neural Adaptive Control	64	(3)
3.3 Flight Control	67	(1)
3.4 X-43A-LS Autolander	68	(5)
3.5 LoFLYTE® Optimal Control	73	(3)
3.6 LoFLYTE® Stability Augmentation	76	(6)
3.7 Design for Uncertainty with Hard Constraints	82	(3)
3.8 Fusion of Soft Computing and Hard Computing	85	(1)
3.9 Conclusions	85	(1)
References	86	(3)
Editor's Introduction to Chapter 4	89	(36)
4 SENSORLESS CONTROL OF SWITCHED RELUCTANCE MOTORS	93	(32)
Adrian David Cheok
4.1 Introduction	93	(2)
4.2 Fuzzy Logic Model	95	(6)
4.2.1 Measurement of Flux Linkage Characteristics	95	(2)
4.2.2 Training and Validation of Fuzzy Model	97	(4)
4.3 Accuracy Enhancement Algorithms	101	(7)
4.3.1 Soft-Computing-Based Optimal Phase Selection	102	(2)
4.3.2 Hard-Computing-Based On-Line Resistance Estimation	104	(1)
4.3.3 Polynomial Predictive Filtering	105	(3)
4.4 Simulation Algorithm and Results	108	(1)
4.5 Hardware and Software Implementation	109	(2)
4.5.1 Hardware Configuration	109	(1)
4.5.2 Software Implementation	110	(1)
4.6 Experimental Results	111	(8)
4.6.1 Acceleration from Zero Speed	112	(1)
4.6.2 Low-Current Low-Speed Test	113	(1)
4.6.3 High-Speed Test	114	(4)
4.6.4 Test of Step Change of Load	118	(1)
4.7 Fusion of Soft Computing and Hard Computing	119	(3)
4.8 Conclusion and Discussion	122	(1)
References	122	(3)
Editor's Introduction to Chapter 5	125	(40)
5 ESTIMATION OF UNCERTAINTY BOUNDS FOR LINEAR AND NONLINEAR ROBUST CONTROL	129	(36)
Gregory D. Buckner
5.1 Introduction	129	(1)
5.2 Robust Control of Active Magnetic Bearings	130	(3)
5.2.1 Active Magnetic Bearing Test Rig	132	(1)
5.3 Nominal H~ Control of the AMB Test Rig	133	(5)
5.3.1 Parametric System Identification	133	(2)
5.3.2 Uncertainty Bound Specification	135	(2)
5.3.3 Nominal Ho. Control: Experimental Results	137	(1)
5.4 Estimating Modeling Uncertainty for He. Control of the AMB Test Rig	138	(10)
5.4.1 Model Error Modeling	140	(1)
5.4.2 Intelligent Model Error Identification	141	(5)
5.4.3 Uncertainty Bound Specification	146	(1)
5.4.4 Identified H~ Control: Experimental Results	147	(1)
5.5 Nonlinear Robust Control of the AMB Test Rig	148	(3)
5.5.1 Nominal Sliding Mode Control of the AMB Test Rig	148	(2)
5.5.2 Nominal SMC: Experimental Results	150	(1)
5.6 Estimating Model Uncertainty for SMC of the AMB Test Rig	151	(8)
5.6.1 Intelligent System Identification	151	(4)
5.6.2 Intelligent Model Error Identification	155	(1)
5.6.3 Intelligent SMC: Experimental Results	156	(3)
5.7 Fusion of Soft Computing and Hard Computing	159	(3)
5.8 Conclusion	162	(1)
References	162	(3)
Editor's Introduction to Chapter 6	165	(34)
6 INDIRECT ON-LINE TOOL WEAR MONITORING	169	(30)
Bernhard Sick
6.1 Introduction	169	(3)
6.2 Problem Description and Monitoring Architecture	172	(4)
6.3 State of the Art	176	(8)
6.3.1 Monitoring Techniques Based on Analytical Models	176	(2)
6.3.2 Monitoring Techniques Based on Neural Networks	178	(3)
6.3.3 Monitoring Techniques Based on Fusion of Physical and Neural Network Models	181	(3)
6.4 New Solution	184	(5)
6.4.1 Solution Outline	184	(1)
6.4.2 Physical Force Model at Digital Preprocessing Level	185	(2)
6.4.3 Dynamic Neural Network at Wear Model Level	187	(2)
6.5 Experimental Results	189	(3)
6.6 Fusion of Soft Computing and Hard Computing	192	(2)
6.7 Summary and Conclusions	194	(1)
References	195	(4)
Editor's Introduction to Chapter 7	199	(42)
7 PREDICTIVE FILTERING METHODS FOR POWER SYSTEMS APPLICATIONS	203	(38)
Seppo J. Ovaska
7.1 Introduction	203	(2)
7.2 Multiplicative General-Parameter Filtering	205	(2)
7.3 Genetic Algorithm for Optimizing Filter Tap Cross-Connections	207	(4)
7.4 Design of Multiplierless Basis Filters by Evolutionary Programming	211	(2)
7.5 Predictive Filters for Zero-Crossings Detector	213	(10)
7.5.1 Single 60-Hz Sinusoid Corrupted by Noise	213	(4)
7.5.2 Sequence of 49-, 50-, and 51-Hz Sinusoids Corrupted by Noise	217	(5)
7.5.3 Discussion of Zero-Crossings Detection Application	222	(1)
7.6 Predictive Filters for Current Reference Generators	223	(10)
7.6.1 Sequence of 49-, 50-, and 51-Hz Noisy Sinusoids	225	(4)
7.6.2 Sequence of 49-, 50-, and 51-Hz Noisy Sinusoids Corrupted by Harmonics	229	(1)
7.6.3 Artificial Current Signal Corrupted by Odd Harmonics	230	(2)
7.6.4 Discussion of Current Reference Generation Application	232	(1)
7.7 Fusion of Soft Computing and Hard Computing	233	(1)
7.8 Conclusion	234	(3)
References	237	(2)
Appendix 7.1: Coefficients of 50-Hz Sinusoid-Predictive FIR Filters	239	(2)
Editor's Introduction to Chapter 8	241	(32)
8 INTRUSION DETECTION FOR COMPUTER SECURITY	245	(28)
Sung-Bae Cho and Sang-Jun Han
8.1 Introduction	245	(2)
8.2 Related Works	247	(6)
8.2.1 Neural Computing	248	(1)
8.2.2 Genetic Computing	249	(2)
8.2.3 Fuzzy Logic	251	(2)
8.2.4 Probabilistic Reasoning	253	(1)
8.3 Intrusion Detection with Hybrid Techniques	253	(8)
8.3.1 Overview	254	(1)
8.3.2 Preprocessing with Self-Organizing Map	254	(2)
8.3.3 Behavior Modeling with Hidden Markov Models	256	(3)
8.3.4 Multiple Models Fusion by Fuzzy Logic	259	(2)
8.4 Experimental Results	261	(6)
8.4.1 Preprocessing	261	(2)
8.4.2 Modeling and Intrusion Detection	263	(4)
8.5 Fusion of Soft Computing and Hard Computing	267	(1)
8.6 Concluding Remarks	268	(2)
References	270	(3)
Editor's Introduction to Chapter 9	273	(40)
9 EMOTION GENERATING METHOD ON HUMAN-COMPUTER INTERFACES	277	(36)
Kazuya Mera and Takumi Ichimura
9.1 Introduction	277	(2)
9.2 Emotion Generating Calculations Method	279	(19)
9.2.1 Favorite Value Database	280	(2)
9.2.2 Calculation Pleasure/Displeasure for an Event	282	(2)
9.2.3 Favorite Value of Modified Element	284	(1)
9.2.4 Experimental Result	285	(1)
9.2.5 Complicated Emotion Allocating Method	286	(8)
9.2.6 Dependency Among Emotion Groups	294	(2)
9.2.7 Example of Complicated Emotion Allocating Method	296	(1)
9.2.8 Experimental Results	297	(1)
9.3 Emotion-Oriented Interaction Systems	298	(4)
9.3.1 Facial Expression Generating Method by Neural Network	298	(3)
9.3.2 Assign Rules to the Facial Expressions	301	(1)
9.4 Applications of Emotion-Oriented Interaction Systems	302	(6)
9.4.1 JavaFaceMail	302	(5)
9.4.2 JavaFaceChat	307	(1)
9.5 Fusion of Soft Computing and Hard Computing	308	(2)
9.6 Conclusion	310	(1)
References	311	(2)
Editor's Introduction to Chapter 10	313	(4)
10 INTRODUCTION TO SCIENTIFIC DATA MINING: DIRECT KERNEL METHODS AND APPLICATIONS	317	(46)
Mark J. Embrechts, Boleslaw Szymanski, and Karsten Sternickel
10.1 Introduction	317	(1)
10.2 What Is Data Mining?	318	(49)
10.2.1 Introduction to Data Mining	318	(2)
10.2.2 Scientific Data Mining	320	(1)
10.2.3 The Data Mining Process	321	(1)
10.2.4 Data Mining Methods and Techniques	322	(1)
10.3 Basic Definitions for Data Mining	323	(1)
10.3.1 The MetaNeural Data Format	323	(3)
10.3.2 The "Standard Data Mining Problem"	326	(3)
10.3.3 Predictive Data Mining	329	(4)
10.3.4 Metrics for Assessing Model Quality	333	(2)
10.4 Introduction to Direct Kernel Methods	335	(1)
10.4.1 Data Mining and Machine Learning Dilemmas for Real-World Data	335	(3)
10.4.2 Regression Models Based on the Data Kernel	338	(1)
10.4.3 Kernel Transformations	339	(1)
10.4.4 Dealing with Bias: Centering the Kernel	340	(2)
10.5 Direct Kernel Ridge Regression	342	(1)
10.5.1 Overview	342	(1)
10.5.2 Choosing the Ridge Parameter	343	(1)
10.6 Case Study #1: Predicting the Binding Energy for Amino Acids	344	(2)
10.7 Case Study #2: Predicting the Region of Origin for Italian Olive Oils	346	(4)
10.8 Case Study #3: Predicting Ischemia from Magnetocardiography	350	(1)
10.8.1 Introduction to Magnetocardiography	350	(1)
10.8.2 Data Acquisition and Preprocessing	351	(1)
10.8.3 Predictive Modeling for Binary Classification of Magnetocardiograms	351	(7)
10.8.4 Feature Selection	358	(1)
10.9 Fusion of Soft Computing and Hard Computing	359	(1)
10.10 Conclusions	359	(1)
References	360	(3)
Editor's Introduction to Chapter 11	363	(4)
11 WORLD WIDE WEB USAGE MINING	367	(30)
Ajith Abraham
11.1 Introduction	367	(5)
11.2 Daily and Hourly Web Usage Clustering	372	(6)
11.2.1 Ant Colony Optimization	372	(2)
11.2.2 Fuzzy Clustering Algorithm	374	(2)
11.2.3 Self-Organizing Map	376	(1)
11.2.4 Analysis of Web Data Clusters	377	(1)
11.3 Daily and Hourly Web Usage Analysis	378	(11)
11.3.1 Linear Genetic Programming	379	(3)
11.3.2 Fuzzy Inference Systems	382	(5)
11.3.3 Experimentation Setup, Training, and Performance Evaluation	387	(2)
11.4 Fusion of Soft Computing and Hard Computing	389	(4)
11.5 Conclusions	393	(1)
References	394	(3)
INDEX	397	(12)
ABOUT THE EDITOR	409