LEADER 13953nam 2200745 450 001 9910831042703321 005 20240219143437.0 010 $a0-471-68339-6 010 $a0-471-68340-X 024 7 $a10.1002/9780471683407 035 $a(CKB)1000000000704286 035 $a(SSID)ssj0000384365 035 $a(PQKBManifestationID)12170758 035 $a(PQKBTitleCode)TC0000384365 035 $a(PQKBWorkID)10339332 035 $a(PQKB)10750267 035 $a(CaBNVSL)mat06218879 035 $a(IDAMS)0b0000648184c20b 035 $a(IEEE)6218879 035 $a(OCoLC)798710473 035 $a(PPN)257257853 035 $a(EXLCZ)991000000000704286 100 $a20151221d2012 uy 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aComputationally intelligent hybrid systems $ethe fusion of soft computing and hard computing /$fedited by Seppo J. Ovaska 210 1$aHoboken, New Jersey :$cWiley,$dc2005. 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2012] 215 $a1 PDF (xxiii, 410 pages) $cillustrations 225 1 $aIEEE press series on computational intelligence ;$v3 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-471-47668-4 320 $aIncludes bibliographical references and index. 327 $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. 327 $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. 327 $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. 327 $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. 327 $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. 330 $aThe practical guide to the integration of soft and hard computing for today's engineering applicationsOver the next decade, the fusion of soft and hard computing will play an increasingly important role in the development of intelligent systems for aerospace, electric power generation, and other safety-critical applications. Computationally Intelligent Hybrid Systems is the only book to examine the practical issues involved in the creation of high-performance, cost-effective applications using a synthesis of neural networks, fuzzy systems, and evolutionary computation with traditional computing methods. This uniquely crafted work combines the experience of many internationally recognized experts in the soft and hard computing research worlds to present practicing engineers with the broadest possible array of methodologies for developing innovative and competitive solutions to real-world problems. Each of the chapters illustrates the wide-ranging applicability of the fusion concept in such critical areas as:. Computer security and data mining. Electrical power systems and large-scale plants. Motor drives and tool wear monitoring. User interfaces and the World Wide Web . Aerospace and robust controlThis is an essential guide for practicing engineers, researchers, and R&D managers who wish to create or understand computationally intelligent hybrid systems, as well as an excellent primary source for graduate courses in soft computing, engineering applications of artificial intelligence, and related topics. 410 0$aIEEE press series on computational intelligence ;$v3 606 $aIntelligent control systems 606 $aComputational intelligence 606 $aSoft computing 606 $aMechanical Engineering - General$2HILCC 606 $aMechanical Engineering$2HILCC 606 $aEngineering & Applied Sciences$2HILCC 615 0$aIntelligent control systems 615 0$aComputational intelligence 615 0$aSoft computing 615 7$aMechanical Engineering - General 615 7$aMechanical Engineering 615 7$aEngineering & Applied Sciences 676 $a006.3 701 $aOvaska$b Seppo J.$f1956-$0845661 712 02$aInstitute of Electrical and Electronics Engineers. 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910831042703321 996 $aComputationally intelligent hybrid systems$94121617 997 $aUNINA LEADER 03218nam 2200601I 450 001 9910972881503321 005 20190617112505.0 010 $a9781838671730 010 $a1838671730 010 $a9781838671716 010 $a1838671714 035 $a(CKB)4100000008415740 035 $a(MiAaPQ)EBC5787820 035 $a(UtOrBLW)9781838671716 035 $a(Perlego)954084 035 $a(EXLCZ)994100000008415740 100 $a20190617h20192019 uy 0 101 0 $aeng 135 $aurun||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSelf-learning and adaptive algorithms for business applications $ea guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions /$fZhengbing Hu, Yevgeniy V. Bodyanskiy, and Oleksii K. Tyshchenko 205 $aFirst edition. 210 1$aBingley, UK :$cEmerald Publishing,$d2019. 215 $a1 online resource (117 pages) 225 1 $aEmerald points 311 08$a9781838671747 311 08$a1838671749 320 $aIncludes bibliographical references. 327 $aPrelims -- Introduction -- Review of the problem area -- Adaptive methods of fuzzy clustering -- Kohonen maps and their ensembles for fuzzy clustering tasks -- Simulation results and solutions for practical tasks -- Conclusion -- References. 330 $aIn today's data-driven world, more sophisticated algorithms for data processing are in high demand, mainly when the data cannot be handled with the help of traditional techniques. Self-learning and adaptive algorithms are now widely used by such leading giants that as Google, Tesla, Microsoft, and Facebook in their projects and applications.In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear. Including research relevant to those studying cybernetics, applied mathematics, statistics, engineering, and bioinformatics who are working in the areas of machine learning, artificial intelligence, complex system modeling and analysis, neural networks, and optimization, this is an ideal read for anyone interested in learning more about the fascinating new developments in machine learning. 410 0$aEmerald points. 606 $aBusiness$xData processing 606 $aElectronic data processing 606 $aFuzzy systems 606 $aBusiness & Economics$xResearch & Development$2bisacsh 606 $aNeural networks & fuzzy systems$2bicssc 615 0$aBusiness$xData processing. 615 0$aElectronic data processing. 615 0$aFuzzy systems. 615 7$aBusiness & Economics$xResearch & Development. 615 7$aNeural networks & fuzzy systems. 676 $a004 700 $aHu$b Zhengbing$0898601 702 $aBodyanskiy$b Yevgeniy V. 702 $aTyshchenko$b Oleksii 801 0$bUtOrBLW 801 1$bUtOrBLW 906 $aBOOK 912 $a9910972881503321 996 $aSelf-learning and adaptive algorithms for business applications$94354066 997 $aUNINA