Eberhart / Shi Computational Intelligence

Concepts to Implementations
1. Auflage 2011
ISBN: 978-0-08-055383-2
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 496 Seiten, Web PDF

The 'soft' analytic tools that comprise the field of computational intelligence have matured to the extent that they can, often in powerful combination with one another, form the foundation for a variety of solutions suitable for use by domain experts without extensive programming experience. Computational Intelligence: Concepts to Implementations provides the conceptual and practical knowledge necessary to develop solutions of this kind. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational intelligence that has, in their extensive experience, proved highly effective. Russ Eberhart and Yuhui Shi have succeeded in integrating various natural and engineering disciplines to establish Computational Intelligence. This is the first comprehensive textbook, including lots of practical examples. -Shun-ichi Amari, RIKEN Brain Science Institute, Japan This book is an excellent choice on its own, but, as in my case, will form the foundation for our advanced graduate courses in the CI disciplines. -James M. Keller, University of Missouri-Columbia The excellent new book by Eberhart and Shi asserts that computational intelligence rests on a foundation of evolutionary computation. This refreshing view has set the book apart from other books on computational intelligence. The book has an emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. -Xin Yao, The Centre of Excellence for Research in Computational Intelligence and Applications, Birmingham
Moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific concepts and paradigms that inform the authors' methodologiesExplores a number of key themes, including self-organization, complex adaptive systems, and emergent computationDetails the metrics and analytical tools needed to assess the performance of computational intelligence toolsConcludes with a series of case studies that illustrate a wide range of successful applicationsPresents code examples in C and C++Provides, at the end of each chapter, review questions and exercises suitable for graduate students, as well as researchers and practitioners engaged in self-studyMakes available, on a companion website, a number of software implementations that can be adapted for real-world applications

Russ Eberhart is Associate Dean of Research at Purdue School of Engineering and Technology in Indianapolis, IN. He is the author of Neural Network PC Tools (Academic Press), a leading book in the field of Neural Networks. Among his credits, he is the former President of the IEEE Neural Networks Council.

Autoren/Hrsg.

Eberhart, Russell C.

Shi, Yuhui

Inhaltsverzeichnis

1;Front Cover;1
2;Computational Intelligence;4
3;Copyright Page;5
4;Table of Contents;6
5;Preface;14
6;Chapter 1. Foundations;22
6.1;Definitions;23
6.2;Biological Basis for Neural Networks;25
6.3;Behavioral Motivations for Fuzzy Logic;30
6.4;Myths about Computational Intelligence;31
6.5;Computational Intelligence Application Areas;32
6.6;Summary;35
6.7;Exercises;35
7;Chapter 2. Computational Intelligence;38
7.1;Adaptation;39
7.2;Self-organization and Evolution;47
7.3;Historical Views of Computational Intelligence;50
7.4;Computational Intelligence as Adaptation and Self-organization;51
7.5;The Ability to Generalize;55
7.6;Computational Intelligence and Soft Computing versus Artificial Intelligence and Hard Computing;56
7.7;Summary;57
7.8;Exercises;59
8;Chapter 3. Evolutionary Computation Concepts and Paradigms;60
8.1;History of Evolutionary Computation;61
8.2;Evolutionary Computation Overview;68
8.3;Genetic Algorithms;72
8.4;Evolutionary Programming;89
8.5;Evolution Strategies;96
8.6;Genetic Programming;102
8.7;Particle Swarm Optimization;108
8.8;Summary;113
8.9;Exercises;114
9;Chapter 4. Evolutionary Computation Implementations;116
9.1;Implementation Issues;118
9.2;Genetic Algorithm Implementation;124
9.3;Particle Swarm Optimization Implementation;139
9.4;Summary;163
9.5;Exercises;163
10;Chapter 5. Neural Network Concepts and Paradigms;166
10.1;Neural Network History;167
10.2;What Neural Networks are and Why They are Useful;186
10.3;Neural Network Components and Terminology;189
10.4;Neural Network Topologies;197
10.5;Neural Network Adaptation;200
10.6;Comparing Neural Networks and Other Information Processing Methods;209
10.7;Preprocessing;211
10.8;Postprocessing;216
10.9;Summary;217
10.10;Exercises;217
11;Chapter 6. Neural Network Implementations;218
11.1;Implementation Issues;219
11.2;Back-propagation Implementation;239
11.3;The Kohonen Network Implementations;256
11.4;Evolutionary Back-propagation Network Implementation;283
11.5;Summary;286
11.6;Exercises;286
12;Chapter 7. Fuzzy Systems Conceptsand Paradigms;290
12.1;History;291
12.2;Fuzzy Sets and Fuzzy Logic;296
12.3;The Theory of Fuzzy Sets;298
12.4;Approximate Reasoning;304
12.5;Developing a Fuzzy Controller;322
12.6;Summary;334
12.7;Exercises;335
13;Chapter 8. Fuzzy Systems Implementations;336
13.1;Implementation Issues;337
13.2;Fuzzy Rule System Implementation;341
13.3;Evolving Fuzzy Rule Systems;374
13.4;Summary;392
13.5;Exercises;392
14;Chapter 9. Computational Intelligence Implementations;394
14.1;Implementation Issues;395
14.2;Fuzzy Evolutionary Fuzzy Rule System Implementation;399
14.3;Choosing the Best Tools;403
14.4;Applying Computational Intelligence to Data Mining;406
14.5;Summary;408
14.6;Exercises;409
15;Chapter 10. Performance Metrics;410
15.1;General Issues;411
15.2;Percent Correct;416
15.3;Average Sum-squared Error;417
15.4;Absolute Error;419
15.5;Normalized Error;420
15.6;Evolutionary Algorithm Effectiveness Metrics;421
15.7;Mann–Whitney U Test;422
15.8;Receiver Operating Characteristic Curves;425
15.9;Recall and Precision;429
15.10;Other ROC-related Measures;430
15.11;Confusion Matrices;431
15.12;Chi-square Test;435
15.13;Summary;438
15.14;Exercises;438
16;Chapter 11. Analysis and Explanation;442
16.1;Sensitivity Analysis;443
16.2;Hinton Diagrams;448
16.3;Computational Intelligence Tools for Explanation Facilities;450
16.4;Summary;458
16.5;Exercises;459
17;Bibliography;460
18;Index;476
19;About the Authors;490
20;Chapter 12. Case Study Summaries;492
20.1;Case Study Preview;493
20.2;Case Study 1: Detection of Electroencephalogram Spikes;495
20.3;Case Study 2: Determining Battery State of Charge;502
20.4;Case Study 3: Schedule Optimization;505
20.5;Case Study 4: Control System Design;521
21;Summary;528
22;Exercises;528
23;Glossary;530