Computational Intelligence

Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing

Inbunden, Engelska, 2013

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Beskrivning

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® exercises and applications in each chapterPresents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systemsConsiders real world problems in the domain of systems modelling, control and optimizationContains a foreword written by Lotfi ZadehComputational 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.

Produktinformation

Utgivningsdatum:2013-05-10
Mått:175 x 252 x 31 mm
Vikt:943 g
Format:Inbunden
Språk:Engelska
Antal sidor:544
Förlag:John Wiley & Sons Inc
ISBN:9781118337844

Utforska kategorier

Artificiell intelligens inom Data och IT

Mer om författaren

Nazmul Siddique is a lecturer in the School of Computing and Intelligent Systems at the University of Ulster. He has published over 120 scientific research papers in journals and conferences including seven book chapters and two books. He is a senior member of the IEEE and has been involved in organising many international conferences. He is on the editorial board of the International Journal of Neural Systems, International Journal of Automation and Control Engineering, Journal of Behavioural Robotics, and Engineering Letters.Hojjat Adeli is the holder of Abba G. Lichtenstein Professorship at The Ohio State University (OSU). He is the Editor-in-Chief of three journals: Computer-Aided Civil and Infrastructure Engineering, Integrated Computer-Aided Engineering, and International Journal of Neural Systems. He has authored over 500 publications including 14 books and has won numerous awards. He is a Distinguished Member of ASCE, a Fellow of AAAS and IEEE. In April 2010 he was profiled as an engineering legend in the journal Leadership and Management in Engineering.

Innehållsförteckning

Foreword xiiiPreface xvAcknowledgements xix1 Introduction to Computational Intelligence 11.1 Computational Intelligence 11.2 Paradigms of Computational Intelligence 21.3 Approaches to Computational Intelligence 31.3.1 Fuzzy Logic 41.3.2 Neural Networks 51.3.3 Evolutionary Computing 51.3.4 Learning Theory 61.3.5 Probabilistic Methods 61.3.6 Swarm Intelligence 71.4 Synergies of Computational Intelligence Techniques 111.5 Applications of Computational Intelligence 121.6 Grand Challenges of Computational Intelligence 131.7 Overview of the Book 131.8 MATLAB R○ Basics 14References 152 Introduction to Fuzzy Logic 192.1 Introduction 192.2 Fuzzy Logic 202.3 Fuzzy Sets 212.4 Membership Functions 222.4.1 Triangular MF 232.4.2 Trapezoidal MF 232.4.3 Gaussian MF 242.4.4 Bell-shaped MF 242.4.5 Sigmoidal MF 262.5 Features of MFs 272.5.1 Support 272.5.2 Core 272.5.3 Fuzzy Singleton 272.5.4 Crossover Point 282.6 Operations on Fuzzy Sets 292.7 Linguistic Variables 332.7.1 Features of Linguistic Variables 332.8 Linguistic Hedges 352.9 Fuzzy Relations 372.9.1 Compositional Rule of Inference 382.10 Fuzzy If–Then Rules 392.10.1 Rule Forms 402.10.2 Compound Rules 402.10.3 Aggregation of Rules 412.11 Fuzzification 432.12 Defuzzification 442.13 Inference Mechanism 482.13.1 Mamdani Fuzzy Inference 492.13.2 Sugeno Fuzzy Inference 502.13.3 Tsukamoto Fuzzy Inference 532.14 Worked Examples 542.15 MATLAB R○ Programs 61References 613 Fuzzy Systems and Applications 653.1 Introduction 653.2 Fuzzy System 663.3 Fuzzy Modelling 673.3.1 Structure Identification 673.3.2 Parameter Identification 703.3.3 Construction of Parameterized Membership Functions 703.4 Fuzzy Control 753.4.1 Fuzzification 753.4.2 Inference Mechanism 763.4.3 Rule Base 783.4.4 Defuzzification 803.5 Design of Fuzzy Controller 813.5.1 Input/Output Selection 823.5.2 Choice of Membership Functions 823.5.3 Creation of Rule Base 823.5.4 Types of Fuzzy Controller 833.6 Modular Fuzzy Controller 973.7 MATLAB R○ Programs 99References 1004 Neural Networks 1034.1 Introduction 1034.2 Artificial Neuron Model 1064.3 Activation Functions 1074.4 Network Architecture 1084.4.1 Feedforward Networks 1094.5 Learning in Neural Networks 1244.5.1 Supervised Learning 1244.5.2 Unsupervised Learning 1384.6 Recurrent Neural Networks 1494.6.1 Elman Networks 1504.6.2 Jordan Networks 1524.6.3 Hopfield Networks 1534.7 MATLAB R○ Programs 155References 1565 Neural Systems and Applications 1595.1 Introduction 1595.2 System Identification and Control 1605.2.1 System Description 1605.2.2 System Identification 1605.2.3 System Control 1615.3 Neural Networks for Control 1635.3.1 System Identification for Control Design 1645.3.2 Neural Networks for Control Design 1655.4 MATLAB R○ Programs 179References 1806 Evolutionary Computing 1836.1 Introduction 1836.2 Evolutionary Computing 1836.3 Terminologies of Evolutionary Computing 1856.3.1 Chromosome Representation 1856.3.2 Encoding Schemes 1866.3.3 Population 1916.3.4 Evaluation (or Fitness) Functions 1936.3.5 Fitness Scaling 1946.4 Genetic Operators 1946.4.1 Selection Operators 1956.4.2 Crossover Operators 1986.4.3 Mutation Operators 2066.5 Performance Measures of EA 2086.6 Evolutionary Algorithms 2096.6.1 Evolutionary Programming 2096.6.2 Evolution Strategies 2136.6.3 Genetic Algorithms 2186.6.4 Genetic Programming 2236.6.5 Differential Evolution 2306.6.6 Cultural Algorithm 2336.7 MATLAB R○ Programs 234References 2357 Evolutionary Systems 2397.1 Introduction 2397.2 Multi-objective Optimization 2437.2.1 Vector-Evaluated GA 2467.2.2 Multi-objective GA 2477.2.3 Niched Pareto GA 2477.2.4 Non-dominated Sorting GA 2487.2.5 Strength Pareto Evolutionary Algorithm 2497.3 Co-evolution 2507.3.1 Cooperative Co-evolution 2537.3.2 Competitive Co-evolution 2557.4 Parallel Evolutionary Algorithm 2567.4.1 Global GA 2577.4.2 Migration (or Island) Model GA 2587.4.3 Diffusion GA 2597.4.4 Hybrid Parallel GA 261References 2628 Evolutionary Fuzzy Systems 2658.1 Introduction 2658.2 Evolutionary Adaptive Fuzzy Systems 2678.2.1 Evolutionary Tuning of Fuzzy Systems 2688.2.2 Evolutionary Learning of Fuzzy Systems 2818.3 Objective Functions and Evaluation 2878.3.1 Objective Functions 2878.3.2 Evaluation 2898.4 Fuzzy Adaptive Evolutionary Algorithms 2908.4.1 Fuzzy Logic-Based Control of EA Parameters 2928.4.2 Fuzzy Logic-Based Genetic Operators of EA 302References 3039 Evolutionary Neural Networks 3079.1 Introduction 3079.2 Supportive Combinations 3099.2.1 NN-EA Supportive Combination 3099.2.2 EA-NN Supportive Combination 3109.3 Collaborative Combinations 3189.3.1 EA for NN Connection Weight Training 3199.3.2 EA for NN Architectures 3269.3.3 EA for NN Node Transfer Functions 3389.3.4 EA for NN Weight, Architecture and Transfer Function Training 3419.4 Amalgamated Combination 3439.5 Competing Conventions 345References 35110 Neural Fuzzy Systems 35710.1 Introduction 35710.2 Combination of Neural and Fuzzy Systems 35910.3 Cooperative Neuro-Fuzzy Systems 36010.3.1 Cooperative FS-NN Systems 36110.3.2 Cooperative NN-FS Systems 36210.4 Concurrent Neuro-Fuzzy Systems 36910.5 Hybrid Neuro-Fuzzy Systems 36910.5.1 Fuzzy Neural Networks with Mamdani-Type Fuzzy Inference System 37010.5.2 Fuzzy Neural Networks with Takagi–Sugeno-type Fuzzy Inference System 37210.5.3 Fuzzy Neural Networks with Tsukamoto-Type Fuzzy Inference System 37310.5.4 Neural Network-Based Fuzzy System (Pi–Sigma Network) 37710.5.5 Fuzzy-Neural System Architecture with Ellipsoid Input Space 38010.5.6 Fuzzy Adaptive Learning Control Network (FALCON) 38210.5.7 Approximate Reasoning-Based Intelligent Control (ARIC) 38410.5.8 Generalized ARIC (GARIC) 38810.5.9 Fuzzy Basis Function Networks (FBFN) 39310.5.10 Fuzzy Net (FUN) 39610.5.11 Combination of Fuzzy Inference and Neural Network in Fuzzy Inference Software (FINEST) 39710.5.12 Neuro-Fuzzy Controller (NEFCON) 40010.5.13 Self-constructing Neural Fuzzy Inference Network (SONFIN) 40110.6 Adaptive Neuro-Fuzzy System 40410.6.1 Adaptive Neuro-Fuzzy Inference System (ANFIS) 40410.6.2 Coactive Neuro-Fuzzy Inference System (CANFIS) 40710.7 Fuzzy Neurons 40910.8 MATLAB R○ Programs 411References 412Appendix A: MATLAB R○ Basics 415Appendix B: MATLAB R○ Programs for Fuzzy Logic 433Appendix C: MATLAB R○ Programs for Fuzzy Systems 443Appendix D: MATLAB R○ Programs for Neural Systems 461Appendix E: MATLAB R○ Programs for Neural Control Design 473Appendix F: MATLAB R○ Programs for Evolutionary Algorithms 489Appendix G: MATLAB R○ Programs for Neuro-Fuzzy Systems 497Index 507