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Computational Intelligence
Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing
Nazmul Siddique (Author), Hojjat Adeli (Author)
9781118337844, Wiley
Hardback, published 10 May 2013
544 pages
25.2 x 17.5 x 3.1 cm, 0.943 kg
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: 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.
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 1.3.1 Fuzzy Logic 4 1.3.2 Neural Networks 5 1.3.3 Evolutionary Computing 5 1.3.4 Learning Theory 6 1.3.5 Probabilistic Methods 6 1.3.6 Swarm Intelligence 7 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 2.4.1 Triangular MF 23 2.4.2 Trapezoidal MF 23 2.4.3 Gaussian MF 24 2.4.4 Bell-shaped MF 24 2.4.5 Sigmoidal MF 26 2.5 Features of MFs 27 2.5.1 Support 27 2.5.2 Core 27 2.5.3 Fuzzy Singleton 27 2.5.4 Crossover Point 28 2.6 Operations on Fuzzy Sets 29 2.7 Linguistic Variables 33 2.7.1 Features of Linguistic Variables 33 2.8 Linguistic Hedges 35 2.9 Fuzzy Relations 37 2.9.1 Compositional Rule of Inference 38 2.10 Fuzzy If–Then Rules 39 2.10.1 Rule Forms 40 2.10.2 Compound Rules 40 2.10.3 Aggregation of Rules 41 2.11 Fuzzification 43 2.12 Defuzzification 44 2.13 Inference Mechanism 48 2.13.1 Mamdani Fuzzy Inference 49 2.13.2 Sugeno Fuzzy Inference 50 2.13.3 Tsukamoto Fuzzy Inference 53 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 3.3.1 Structure Identification 67 3.3.2 Parameter Identification 70 3.3.3 Construction of Parameterized Membership Functions 70 3.4 Fuzzy Control 75 3.4.1 Fuzzification 75 3.4.2 Inference Mechanism 76 3.4.3 Rule Base 78 3.4.4 Defuzzification 80 3.5 Design of Fuzzy Controller 81 3.5.1 Input/Output Selection 82 3.5.2 Choice of Membership Functions 82 3.5.3 Creation of Rule Base 82 3.5.4 Types of Fuzzy Controller 83 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 4.4.1 Feedforward Networks 109 4.5 Learning in Neural Networks 124 4.5.1 Supervised Learning 124 4.5.2 Unsupervised Learning 138 4.6 Recurrent Neural Networks 149 4.6.1 Elman Networks 150 4.6.2 Jordan Networks 152 4.6.3 Hopfield Networks 153 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 5.2.1 System Description 160 5.2.2 System Identification 160 5.2.3 System Control 161 5.3 Neural Networks for Control 163 5.3.1 System Identification for Control Design 164 5.3.2 Neural Networks for Control Design 165 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 6.3.1 Chromosome Representation 185 6.3.2 Encoding Schemes 186 6.3.3 Population 191 6.3.4 Evaluation (or Fitness) Functions 193 6.3.5 Fitness Scaling 194 6.4 Genetic Operators 194 6.4.1 Selection Operators 195 6.4.2 Crossover Operators 198 6.4.3 Mutation Operators 206 6.5 Performance Measures of EA 208 6.6 Evolutionary Algorithms 209 6.6.1 Evolutionary Programming 209 6.6.2 Evolution Strategies 213 6.6.3 Genetic Algorithms 218 6.6.4 Genetic Programming 223 6.6.5 Differential Evolution 230 6.6.6 Cultural Algorithm 233 6.7 MATLAB R○ Programs 234 References 235 7 Evolutionary Systems 239 7.1 Introduction 239 7.2 Multi-objective Optimization 243 7.2.1 Vector-Evaluated GA 246 7.2.2 Multi-objective GA 247 7.2.3 Niched Pareto GA 247 7.2.4 Non-dominated Sorting GA 248 7.2.5 Strength Pareto Evolutionary Algorithm 249 7.3 Co-evolution 250 7.3.1 Cooperative Co-evolution 253 7.3.2 Competitive Co-evolution 255 7.4 Parallel Evolutionary Algorithm 256 7.4.1 Global GA 257 7.4.2 Migration (or Island) Model GA 258 7.4.3 Diffusion GA 259 7.4.4 Hybrid Parallel GA 261 References 262 8 Evolutionary Fuzzy Systems 265 8.1 Introduction 265 8.2 Evolutionary Adaptive Fuzzy Systems 267 8.2.1 Evolutionary Tuning of Fuzzy Systems 268 8.2.2 Evolutionary Learning of Fuzzy Systems 281 8.3 Objective Functions and Evaluation 287 8.3.1 Objective Functions 287 8.3.2 Evaluation 289 8.4 Fuzzy Adaptive Evolutionary Algorithms 290 8.4.1 Fuzzy Logic-Based Control of EA Parameters 292 8.4.2 Fuzzy Logic-Based Genetic Operators of EA 302 References 303 9 Evolutionary Neural Networks 307 9.1 Introduction 307 9.2 Supportive Combinations 309 9.2.1 NN-EA Supportive Combination 309 9.2.2 EA-NN Supportive Combination 310 9.3 Collaborative Combinations 318 9.3.1 EA for NN Connection Weight Training 319 9.3.2 EA for NN Architectures 326 9.3.3 EA for NN Node Transfer Functions 338 9.3.4 EA for NN Weight, Architecture and Transfer Function Training 341 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 10.3.1 Cooperative FS-NN Systems 361 10.3.2 Cooperative NN-FS Systems 362 10.4 Concurrent Neuro-Fuzzy Systems 369 10.5 Hybrid Neuro-Fuzzy Systems 369 10.5.1 Fuzzy Neural Networks with Mamdani-Type Fuzzy Inference System 370 10.5.2 Fuzzy Neural Networks with Takagi–Sugeno-type Fuzzy Inference System 372 10.5.3 Fuzzy Neural Networks with Tsukamoto-Type Fuzzy Inference System 373 10.5.4 Neural Network-Based Fuzzy System (Pi–Sigma Network) 377 10.5.5 Fuzzy-Neural System Architecture with Ellipsoid Input Space 380 10.5.6 Fuzzy Adaptive Learning Control Network (FALCON) 382 10.5.7 Approximate Reasoning-Based Intelligent Control (ARIC) 384 10.5.8 Generalized ARIC (GARIC) 388 10.5.9 Fuzzy Basis Function Networks (FBFN) 393 10.5.10 Fuzzy Net (FUN) 396 10.5.11 Combination of Fuzzy Inference and Neural Network in Fuzzy Inference Software (FINEST) 397 10.5.12 Neuro-Fuzzy Controller (NEFCON) 400 10.5.13 Self-constructing Neural Fuzzy Inference Network (SONFIN) 401 10.6 Adaptive Neuro-Fuzzy System 404 10.6.1 Adaptive Neuro-Fuzzy Inference System (ANFIS) 404 10.6.2 Coactive Neuro-Fuzzy Inference System (CANFIS) 407 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○ Programs for Fuzzy Systems 443 Appendix D: MATLAB R○ Programs for Neural Systems 461 Appendix E: MATLAB R○ Programs for Neural Control Design 473 Appendix F: MATLAB R○ Programs for Evolutionary Algorithms 489 Appendix G: MATLAB R○ Programs for Neuro-Fuzzy Systems 497 Index 507
Subject Areas: Mechanical engineering & materials [TG]
