E-raamat: Hybrid Intelligent Engineering Systems [World Scientific e-raamat]

Preface v 1 An Introduction to Intelligent Systems 1(10) L.C. Jain R. Vemuri 1.1 Introduction 1(1) 1.2 Knowledge-Based Systems(KBS) 2(3) 1.3 Artificial Neural Networks(ANNs) 5(2) 1.4 Fuzzy Logic Systems 7(1) 1.5 Genetic Algorithms(GAs) 8(1) 1.6 Hybrid Systems 8(1) 1.7 Conclusion and Future Directions 9(1) Bibliography 9(2) 2 Integration of Fuzzy Systems and Neural Networks, and Fuzzy Systems and Genetic Algorithms 11(28) H. Takagi M.A. Lee 2.1 Introduction 12(1) 2.2 Neural Networks for Fuzzy Systems 12(7) 2.2.1 NN-driver fuzzy reasoning 13(2) 2.2.2 Tuning of Parameterized fuzzy systems 15(3) 2.2.3 Applications 18(1) 2.3 Fuzzy Systems for Neural Networks 19(4) 2.3.1 Knowledge structure for neural networks 20(1) 2.3.2 Internal analysis to improve capability 21(1) 2.3.3 Applications 22(1) 2.4 Genetic Algorithms for Fuzzy Systems 23(6) 2.4.1 GA Coding for designing FSs 24(3) 2.4.2 Embedding a priori Knowledge 27(1) 2.4.3 Remarks 28(1) 2.5 Fuzzy Systems for Genetic Algorithms 29(6) 2.5.1 Dynamic Parametric GA 30(1) 2.5.2 Designing the dynamic parametric GA 31(2) 2.5.3 Evaluation of the dynamic parametric GA 33(2) 2.5.4 Remarks 35(1) 2.6. Conclusion 35(1) Bibliography 35(4) 3 Neuro-Expert Architecture and Applications in Diagnostic/Classification Domains 39(30) L.R. Medsker 3.1 Introduction 40(1) 3.2 Neural Networks and Expert Systems 41(6) 3.2.1 Expert Systems 41(1) 3.2.2 Neural Computing 42(2) 3.2.3 Hybrid systems 44(2) 3.2.4 Summary 46(1) 3.3 Neuro-Expert System Applications in Industry 47(15) 3.3.1 Working examples 49(6) 3.3.2 Neuro-expert systems integrated with other intelligent technologies 55(7) 3.4 Future for Hybrid Systems 62(1) 3.4.1 Soft Computing 62(1) 3.4.2 Distributed systems 62(1) 3.4.3 Summary 63(1) Bibliography 63(6) 4 Genetic Learning in Fuzzy Control 69(34) C.L. Karr L.C. Jain 4.1 Introduction 70(2) 4.2 Evolutionary Computing 72(2) 4.3 Physical System 74(3) 4.4 Design of a Fuzzy Logic Controller 77(8) 4.5 The Mechanics of a Micro GA 85(5) 4.6 The Design of a Non-Adaptive Liquid Level 90(4) 4.7 An Adaptive FLC for a Time-Varying Environment 94(3) 4.8 Summary and Conclusions 97(1) Bibliography 98(5) 5 Cases in Geno-fuzzy Control 103(30) C.L .Karr L.C. Jain 5.1 Introduction 103(1) 5.2 Cart-Pole Balancing System 104(4) 5.3 Cart-Pole FLC 108(9) 5.4 pH Titration System 117(2) 5.5 pH FLC 119(7) 5.6 Geno-Fuzzy Control of Backpropagation 126(2) 5.7 Potential Areas of Research 128(2) 5.8 Summary and Conclusions 130(1) Bibliography 131(2) 6 Evolutionary Engineering and Applications 133(34) H. de Garis 6.1 Introduction 134(3) 6.2 Evolutionary Engineering 137(1) 6.3 Cellular Automata Based Neural Networks 138(5) 6.4 Further Details 143(2) 6.5 A Billion Neurons in a Trillion Cell CAM by 2001 145(5) 6.6 3D Version 150(1) 6.7 Recent Work 151(3) 6.8 Future Work 154(6) 6.9 Summary 160(5) Bibliography 165(2) 7 Fusion Technology of Neuro, Fuzzy, GA and Chaos Theory and Applications 167 R. Katayama K. Kuwata L.C. Jain 7.1 Introduction 167(1) 7.2 Fusion Technology of Neural Network and Chaos 168(3) 7.3 Fusion Technology of Fuzzy and Chaos 171(2) 7.4 Fusion Technology of Genetic Algorithm and Chaos 173(1) 7.5 Applications of Chaos Theory 173(5) 7.6 Conclusion 178(1) Bibliography 178