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E-raamat: Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing

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(University of Ulster, UK), (Ohio State University)
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  • Ilmumisaeg: 22-Mar-2013
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118534809
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Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary ComputingSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 22-Mar-2013
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118534809
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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 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.
Foreword xiii
Preface xv
Acknowledgements xix
1 Introduction to Computational Intelligence
1(18)
1.1 Computational Intelligence
1(1)
1.2 Paradigms of Computational Intelligence
2(1)
1.3 Approaches to Computational Intelligence
3(8)
1.3.1 Fuzzy Logic
4(1)
1.3.2 Neural Networks
5(1)
1.3.3 Evolutionary Computing
5(1)
1.3.4 Learning Theory
6(1)
1.3.5 Probabilistic Methods
6(1)
1.3.6 Swarm Intelligence
7(4)
1.4 Synergies of Computational Intelligence Techniques
11(1)
1.5 Applications of Computational Intelligence
12(1)
1.6 Grand Challenges of Computational Intelligence
13(1)
1.7 Overview of the Book
13(1)
1.8 MATLAB® Basics
14(5)
References
15(4)
2 Introduction to Fuzzy Logic
19(46)
2.1 Introduction
19(1)
2.2 Fuzzy Logic
20(1)
2.3 Fuzzy Sets
21(1)
2.4 Membership Functions
22(5)
2.4.1 Triangular MF
23(1)
2.4.2 Trapezoidal MF
23(1)
2.4.3 Gaussian MF
24(1)
2.4.4 Bell-shaped MF
24(2)
2.4.5 Sigmoidal MF
26(1)
2.5 Features of MFs
27(2)
2.5.1 Support
27(1)
2.5.2 Core
27(1)
2.5.3 Fuzzy Singleton
27(1)
2.5.4 Crossover Point
28(1)
2.6 Operations on Fuzzy Sets
29(4)
2.7 Linguistic Variables
33(2)
2.7.1 Features of Linguistic Variables
33(2)
2.8 Linguistic Hedges
35(2)
2.9 Fuzzy Relations
37(2)
2.9.1 Compositional Rule of Inference
38(1)
2.10 Fuzzy If-Then Rules
39(4)
2.10.1 Rule Forms
40(1)
2.10.2 Compound Rules
40(1)
2.10.3 Aggregation of Rules
41(2)
2.11 Fuzzification
43(1)
2.12 Defuzzification
44(4)
2.13 Inference Mechanism
48(6)
2.13.1 Mamdani Fuzzy Inference
49(1)
2.13.2 Sugeno Fuzzy Inference
50(3)
2.13.3 Tsukamoto Fuzzy Inference
53(1)
2.14 Worked Examples
54(7)
2.15 MATLAB® Programs
61(4)
References
61(4)
3 Fuzzy Systems and Applications
65(38)
3.1 Introduction
65(1)
3.2 Fuzzy System
66(1)
3.3 Fuzzy Modelling
67(8)
3.3.1 Structure Identification
67(3)
3.3.2 Parameter Identification
70(1)
3.3.3 Construction of Parameterized Membership Functions
70(5)
3.4 Fuzzy Control
75(6)
3.4.1 Fuzzification
75(1)
3.4.2 Inference Mechanism
76(2)
3.4.3 Rule Base
78(2)
3.4.4 Defuzzification
80(1)
3.5 Design of Fuzzy Controller
81(16)
3.5.1 Input/Output Selection
82(1)
3.5.2 Choice of Membership Functions
82(1)
3.5.3 Creation of Rule Base
82(1)
3.5.4 Types of Fuzzy Controller
83(14)
3.6 Modular Fuzzy Controller
97(2)
3.7 MATLAB® Programs
99(4)
References
100(3)
4 Neural Networks
103(56)
4.1 Introduction
103(3)
4.2 Artificial Neuron Model
106(1)
4.3 Activation Functions
107(1)
4.4 Network Architecture
108(16)
4.4.1 Feedforward Networks
109(15)
4.5 Learning in Neural Networks
124(25)
4.5.1 Supervised Learning
124(14)
4.5.2 Unsupervised Learning
138(11)
4.6 Recurrent Neural Networks
149(6)
4.6.1 Elman Networks
150(2)
4.6.2 Jordan Networks
152(1)
4.6.3 Hopfield Networks
153(2)
4.7 MATLAB® Programs
155(4)
References
156(3)
5 Neural Systems and Applications
159(24)
5.1 Introduction
159(1)
5.2 System Identification and Control
160(3)
5.2.1 System Description
160(1)
5.2.2 System Identification
160(1)
5.2.3 System Control
161(2)
5.3 Neural Networks for Control
163(16)
5.3.1 System Identification for Control Design
164(1)
5.3.2 Neural Networks for Control Design
165(14)
5.4 MATLAB® Programs
179(4)
References
180(3)
6 Evolutionary Computing
183(56)
6.1 Introduction
183(1)
6.2 Evolutionary Computing
183(2)
6.3 Terminologies of Evolutionary Computing
185(9)
6.3.1 Chromosome Representation
185(1)
6.3.2 Encoding Schemes
186(5)
6.3.3 Population
191(2)
6.3.4 Evaluation (or Fitness) Functions
193(1)
6.3.5 Fitness Scaling
194(1)
6.4 Genetic Operators
194(14)
6.4.1 Selection Operators
195(3)
6.4.2 Crossover Operators
198(8)
6.4.3 Mutation Operators
206(2)
6.5 Performance Measures of EA
208(1)
6.6 Evolutionary Algorithms
209(25)
6.6.1 Evolutionary Programming
209(4)
6.6.2 Evolution Strategies
213(5)
6.6.3 Genetic Algorithms
218(5)
6.6.4 Genetic Programming
223(7)
6.6.5 Differential Evolution
230(3)
6.6.6 Cultural Algorithm
233(1)
6.7 MATLAB® Programs
234(5)
References
235(4)
7 Evolutionary Systems
239(26)
7.1 Introduction
239(4)
7.2 Multi-objective Optimization
243(7)
7.2.1 Vector-Evaluated GA
246(1)
7.2.2 Multi-objective GA
247(1)
7.2.3 Niched Pareto GA
247(1)
7.2.4 Non-dominated Sorting GA
248(1)
7.2.5 Strength Pareto Evolutionary Algorithm
249(1)
7.3 Co-evolution
250(6)
7.3.1 Cooperative Co-evolution
253(2)
7.3.2 Competitive Co-evolution
255(1)
7.4 Parallel Evolutionary Algorithm
256(9)
7.4.1 Global GA
257(1)
7.4.2 Migration (or Island) Model GA
258(1)
7.4.3 Diffusion GA
259(2)
7.4.4 Hybrid Parallel GA
261(1)
References
262(3)
8 Evolutionary Fuzzy Systems
265(42)
8.1 Introduction
265(2)
8.2 Evolutionary Adaptive Fuzzy Systems
267(20)
8.2.1 Evolutionary Tuning of Fuzzy Systems
268(13)
8.2.2 Evolutionary Learning of Fuzzy Systems
281(6)
8.3 Objective Functions and Evaluation
287(3)
8.3.1 Objective Functions
287(2)
8.3.2 Evaluation
289(1)
8.4 Fuzzy Adaptive Evolutionary Algorithms
290(17)
8.4.1 Fuzzy Logic-Based Control of EA Parameters
292(10)
8.4.2 Fuzzy Logic-Based Genetic Operators of EA
302(1)
References
303(4)
9 Evolutionary Neural Networks
307(50)
9.1 Introduction
307(2)
9.2 Supportive Combinations
309(9)
9.2.1 NN-EA Supportive Combination
309(1)
9.2.2 EA-NN Supportive Combination
310(8)
9.3 Collaborative Combinations
318(25)
9.3.1 EA for NN Connection Weight Training
319(7)
9.3.2 EA for NN Architectures
326(12)
9.3.3 EA for NN Node Transfer Functions
338(3)
9.3.4 EA for NN Weight, Architecture and Transfer Function Training
341(2)
9.4 Amalgamated Combination
343(2)
9.5 Competing Conventions
345(12)
References
351(6)
10 Neural Fuzzy Systems
357(58)
10.1 Introduction
357(2)
10.2 Combination of Neural and Fuzzy Systems
359(1)
10.3 Cooperative Neuro-Fuzzy Systems
360(9)
10.3.1 Cooperative FS-NN Systems
361(1)
10.3.2 Cooperative NN-FS Systems
362(7)
10.4 Concurrent Neuro-Fuzzy Systems
369(1)
10.5 Hybrid Neuro-Fuzzy Systems
369(35)
10.5.1 Fuzzy Neural Networks with Mamdani-Type Fuzzy Inference System
370(2)
10.5.2 Fuzzy Neural Networks with Takagi-Sugeno-type Fuzzy Inference System
372(1)
10.5.3 Fuzzy Neural Networks with Tsukamoto-Type Fuzzy Inference System
373(4)
10.5.4 Neural Network-Based Fuzzy System (Pi-Sigma Network)
377(3)
10.5.5 Fuzzy-Neural System Architecture with Ellipsoid Input Space
380(2)
10.5.6 Fuzzy Adaptive Learning Control Network (FALCON)
382(2)
10.5.7 Approximate Reasoning-Based Intelligent Control (ARIC)
384(4)
10.5.8 Generalized ARIC (GARIC)
388(5)
10.5.9 Fuzzy Basis Function Networks (FBFN)
393(3)
10.5.10 Fuzzy Net (FUN)
396(1)
10.5.11 Combination of Fuzzy Inference and Neural Network in Fuzzy Inference Software (FINEST)
397(3)
10.5.12 Neuro-Fuzzy Controller (NEFCON)
400(1)
10.5.13 Self-constructing Neural Fuzzy Inference Network (SONFIN)
401(3)
10.6 Adaptive Neuro-Fuzzy System
404(5)
10.6.1 Adaptive Neuro-Fuzzy Inference System (ANFIS)
404(3)
10.6.2 Coactive Neuro-Fuzzy Inference System (CANFIS)
407(2)
10.7 Fuzzy Neurons
409(2)
10.8 MATLAB® Programs
411(4)
References
412(3)
Appendix A MATLAB® Basics 415(18)
Appendix B MATLAB® Programs for Fuzzy Logic 433(10)
Appendix C MATLAB® Programs for Fuzzy Systems 443(18)
Appendix D MATLAB® Programs for Neural Systems 461(12)
Appendix E MATLAB® Programs for Neural Control Design 473(16)
Appendix F MATLAB® Programs for Evolutionary Algorithms 489(8)
Appendix G MATLAB® Programs for Neuro-Fuzzy Systems 497(10)
Index 507
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.
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