Computational Intelligence
Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing
Lieferzeit: Besorgungstitel - Lieferbar innerhalb von 10 Werktagen I
Gewicht:
930 g
Format:
246x173x30 mm
Beschreibung:
Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples.
Key features:
* Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB(r) exercises and applications in each chapter
* Presents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systems
* Considers real world problems in the domain of systems modelling, control and optimization
* Contains a foreword written by Lotfi Zadeh
Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.
Key features:
* Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB(r) exercises and applications in each chapter
* Presents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systems
* Considers real world problems in the domain of systems modelling, control and optimization
* Contains a foreword written by Lotfi Zadeh
Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.
Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence.
Foreword xiii
Preface xv
Acknowledgements xix
1 Introduction to Computational Intelligence 1
1.1 Computational Intelligence 1
1.2 Paradigms of Computational Intelligence 2
1.3 Approaches to Computational Intelligence 3
1.4 Synergies of Computational Intelligence Techniques 11
1.5 Applications of Computational Intelligence 12
1.6 Grand Challenges of Computational Intelligence 13
1.7 Overview of the Book 13
1.8 MATLAB R _ Basics 14
References 15
2 Introduction to Fuzzy Logic 19
2.1 Introduction 19
2.2 Fuzzy Logic 20
2.3 Fuzzy Sets 21
2.4 Membership Functions 22
2.5 Features of MFs 27
2.6 Operations on Fuzzy Sets 29
2.7 Linguistic Variables 33
2.8 Linguistic Hedges 35
2.9 Fuzzy Relations 37
2.10 Fuzzy If-Then Rules 39
2.11 Fuzzification 43
2.12 Defuzzification 44
2.13 Inference Mechanism 48
2.14 Worked Examples 54
2.15 MATLAB R _ Programs 61
References 61
3 Fuzzy Systems and Applications 65
3.1 Introduction 65
3.2 Fuzzy System 66
3.3 Fuzzy Modelling 67
3.4 Fuzzy Control 75
3.5 Design of Fuzzy Controller 81
3.6 Modular Fuzzy Controller 97
3.7 MATLAB R _ Programs 99
References 100
4 Neural Networks 103
4.1 Introduction 103
4.2 Artificial Neuron Model 106
4.3 Activation Functions 107
4.4 Network Architecture 108
4.5 Learning in Neural Networks 124
4.6 Recurrent Neural Networks 149
4.7 MATLAB R _ Programs 155
References 156
5 Neural Systems and Applications 159
5.1 Introduction 159
5.2 System Identification and Control 160
5.3 Neural Networks for Control 163
5.4 MATLAB R _ Programs 179
References 180
6 Evolutionary Computing 183
6.1 Introduction 183
6.2 Evolutionary Computing 183
6.3 Terminologies of Evolutionary Computing 185
6.4 Genetic Operators 194
6.5 Performance Measures of EA 208
6.6 Evolutionary Algorithms 209
6.7 MATLAB R _ Programs 234
References 235
7 Evolutionary Systems 239
7.1 Introduction 239
7.2 Multi-objective Optimization 243
7.3 Co-evolution 250
7.4 Parallel Evolutionary Algorithm 256
References 262
8 Evolutionary Fuzzy Systems 265
8.1 Introduction 265
8.2 Evolutionary Adaptive Fuzzy Systems 267
8.3 Objective Functions and Evaluation 287
8.4 Fuzzy Adaptive Evolutionary Algorithms 290
References 303
9 Evolutionary Neural Networks 307
9.1 Introduction 307
9.2 Supportive Combinations 309
9.3 Collaborative Combinations 318
9.4 Amalgamated Combination 343
9.5 Competing Conventions 345
References 351
10 Neural Fuzzy Systems 357
10.1 Introduction 357
10.2 Combination of Neural and Fuzzy Systems 359
10.3 Cooperative Neuro-Fuzzy Systems 360
10.4 Concurrent Neuro-Fuzzy Systems 369
10.5 Hybrid Neuro-Fuzzy Systems 369
10.6 Adaptive Neuro-Fuzzy System 404
10.7 Fuzzy Neurons 409
10.8 MATLAB R _ Programs 411
References 412
Appendix A: MATLAB R _ Basics 415
Appendix B: MATLAB R _ Programs for Fuzzy Logic 433
Appendix C: MATLAB R _ P
Preface xv
Acknowledgements xix
1 Introduction to Computational Intelligence 1
1.1 Computational Intelligence 1
1.2 Paradigms of Computational Intelligence 2
1.3 Approaches to Computational Intelligence 3
1.4 Synergies of Computational Intelligence Techniques 11
1.5 Applications of Computational Intelligence 12
1.6 Grand Challenges of Computational Intelligence 13
1.7 Overview of the Book 13
1.8 MATLAB R _ Basics 14
References 15
2 Introduction to Fuzzy Logic 19
2.1 Introduction 19
2.2 Fuzzy Logic 20
2.3 Fuzzy Sets 21
2.4 Membership Functions 22
2.5 Features of MFs 27
2.6 Operations on Fuzzy Sets 29
2.7 Linguistic Variables 33
2.8 Linguistic Hedges 35
2.9 Fuzzy Relations 37
2.10 Fuzzy If-Then Rules 39
2.11 Fuzzification 43
2.12 Defuzzification 44
2.13 Inference Mechanism 48
2.14 Worked Examples 54
2.15 MATLAB R _ Programs 61
References 61
3 Fuzzy Systems and Applications 65
3.1 Introduction 65
3.2 Fuzzy System 66
3.3 Fuzzy Modelling 67
3.4 Fuzzy Control 75
3.5 Design of Fuzzy Controller 81
3.6 Modular Fuzzy Controller 97
3.7 MATLAB R _ Programs 99
References 100
4 Neural Networks 103
4.1 Introduction 103
4.2 Artificial Neuron Model 106
4.3 Activation Functions 107
4.4 Network Architecture 108
4.5 Learning in Neural Networks 124
4.6 Recurrent Neural Networks 149
4.7 MATLAB R _ Programs 155
References 156
5 Neural Systems and Applications 159
5.1 Introduction 159
5.2 System Identification and Control 160
5.3 Neural Networks for Control 163
5.4 MATLAB R _ Programs 179
References 180
6 Evolutionary Computing 183
6.1 Introduction 183
6.2 Evolutionary Computing 183
6.3 Terminologies of Evolutionary Computing 185
6.4 Genetic Operators 194
6.5 Performance Measures of EA 208
6.6 Evolutionary Algorithms 209
6.7 MATLAB R _ Programs 234
References 235
7 Evolutionary Systems 239
7.1 Introduction 239
7.2 Multi-objective Optimization 243
7.3 Co-evolution 250
7.4 Parallel Evolutionary Algorithm 256
References 262
8 Evolutionary Fuzzy Systems 265
8.1 Introduction 265
8.2 Evolutionary Adaptive Fuzzy Systems 267
8.3 Objective Functions and Evaluation 287
8.4 Fuzzy Adaptive Evolutionary Algorithms 290
References 303
9 Evolutionary Neural Networks 307
9.1 Introduction 307
9.2 Supportive Combinations 309
9.3 Collaborative Combinations 318
9.4 Amalgamated Combination 343
9.5 Competing Conventions 345
References 351
10 Neural Fuzzy Systems 357
10.1 Introduction 357
10.2 Combination of Neural and Fuzzy Systems 359
10.3 Cooperative Neuro-Fuzzy Systems 360
10.4 Concurrent Neuro-Fuzzy Systems 369
10.5 Hybrid Neuro-Fuzzy Systems 369
10.6 Adaptive Neuro-Fuzzy System 404
10.7 Fuzzy Neurons 409
10.8 MATLAB R _ Programs 411
References 412
Appendix A: MATLAB R _ Basics 415
Appendix B: MATLAB R _ Programs for Fuzzy Logic 433
Appendix C: MATLAB R _ P
Computational Intelligence: Synergies of Fuzzy Logic, NeuralNetworks and Evolutionary Computing presents an introduction tosome of the cutting edge technological paradigms under the umbrellaof computational intelligence.