| 000 | 15429nam a2201237 i 4500 | ||
|---|---|---|---|
| 001 | 6218879 | ||
| 003 | IEEE | ||
| 005 | 20220712205828.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151221s2012 njua ob 001 eng d | ||
| 020 |
_a9780471683407 _qebook |
||
| 020 |
_z9780471476689 _qprint |
||
| 020 |
_z047168340X _qelectronic |
||
| 024 | 7 |
_a10.1002/9780471683407 _2doi |
|
| 035 | _a(CaBNVSL)mat06218879 | ||
| 035 | _a(IDAMS)0b0000648184c20b | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aTJ217.5 _b.C667 2005eb |
|
| 082 | 0 | 4 |
_a006.3 _222 |
| 245 | 0 | 0 |
_aComputationally intelligent hybrid systems : _bthe fusion of soft computing and hard computing / _cedited by Seppo J. Ovaska. |
| 264 | 1 |
_aHoboken, New Jersey : _bWiley, _cc2005. |
|
| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2012] |
|
| 300 |
_a1 PDF (xxiii, 410 pages) : _billustrations. |
||
| 336 |
_atext _2rdacontent |
||
| 337 |
_aelectronic _2isbdmedia |
||
| 338 |
_aonline resource _2rdacarrier |
||
| 490 | 1 |
_aIEEE press series on computational intelligence ; _v3 |
|
| 504 | _aIncludes bibliographical references and index. | ||
| 505 | 0 | _aContributors 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. | |
| 505 | 8 | _a6 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. | |
| 505 | 8 | _a11 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 409Contributors 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. | |
| 505 | 8 | _a5 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. | |
| 505 | 8 | _a10 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. | |
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aThe practical guide to the integration of soft and hard computing for today's engineering applicationsOver the next decade, the fusion of soft and hard computing will play an increasingly important role in the development of intelligent systems for aerospace, electric power generation, and other safety-critical applications. Computationally Intelligent Hybrid Systems is the only book to examine the practical issues 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. 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 is an essential guide for practicing engineers, researchers, and R&D managers who wish to create or understand computationally intelligent hybrid systems, as well as an excellent primary source for graduate courses in soft computing, engineering applications of artificial intelligence, and related topics. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/21/2015. | ||
| 650 | 0 |
_aIntelligent control systems. _93412 |
|
| 650 | 0 |
_aHybrid systems. _919370 |
|
| 650 | 0 |
_aComputational intelligence. _97716 |
|
| 650 | 0 |
_aSoft computing. _93731 |
|
| 655 | 0 |
_aElectronic books. _93294 |
|
| 695 | _aAccuracy | ||
| 695 | _aAdaptive control | ||
| 695 | _aAdaptive filters | ||
| 695 | _aAerospace control | ||
| 695 | _aAerospace electronics | ||
| 695 | _aApproximation algorithms | ||
| 695 | _aBand pass filters | ||
| 695 | _aBiographies | ||
| 695 | _aClustering algorithms | ||
| 695 | _aComputer security | ||
| 695 | _aComputers | ||
| 695 | _aCouplings | ||
| 695 | _aData mining | ||
| 695 | _aDatabases | ||
| 695 | _aDynamic programming | ||
| 695 | _aEstimation | ||
| 695 | _aFiltering algorithms | ||
| 695 | _aFinite impulse response filter | ||
| 695 | _aFlywheels | ||
| 695 | _aForce | ||
| 695 | _aForecasting | ||
| 695 | _aHeuristic algorithms | ||
| 695 | _aHuman computer interaction | ||
| 695 | _aHuman factors | ||
| 695 | _aIndexes | ||
| 695 | _aInference algorithms | ||
| 695 | _aIntrusion | ||
| 695 | _aKnowledge engineering | ||
| 695 | _aLoad forecasting | ||
| 695 | _aLoad modeling | ||
| 695 | _aMachine tools | ||
| 695 | _aMagnetic levitation | ||
| 695 | _aMaterials | ||
| 695 | _aMathematical model | ||
| 695 | _aMonitoring | ||
| 695 | _aNeural networks | ||
| 695 | _aPhysiology | ||
| 695 | _aPower harmonic filters | ||
| 695 | _aPredictive models | ||
| 695 | _aReluctance motors | ||
| 695 | _aResistance | ||
| 695 | _aRobust control | ||
| 695 | _aRotors | ||
| 695 | _aSections | ||
| 695 | _aSensors | ||
| 695 | _aSignal processing algorithms | ||
| 695 | _aStandards | ||
| 695 | _aTrajectory | ||
| 695 | _aTurning | ||
| 695 | _aUncertainty | ||
| 695 | _aWeb servers | ||
| 695 | _aWeb sites | ||
| 695 | _aWindings | ||
| 700 | 1 |
_aOvaska, Seppo J., _d1956- _927991 |
|
| 710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. _927992 |
|
| 710 | 2 |
_aInstitute of Electrical and Electronics Engineers. _99191 |
|
| 710 | 2 |
_aWiley InterScience (Online service), _epublisher. _96290 |
|
| 776 | 0 | 8 |
_iPrint version: _z9780471476689 |
| 830 | 0 |
_aIEEE press series on computational intelligence ; _v3 _927993 |
|
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6218879 |
| 942 | _cEBK | ||
| 999 |
_c74236 _d74236 |
||