Computational Intelligence: Concepts to Implementations
Eberhart, Russell C.; Shi, Yuhui
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Table of contents
- Cover
- Table of Contentsv
- Prefacexiii
- Chapter 1. Foundations1
- Definitions2
- Biological Basis for Neural Networks4
- Behavioral Motivations for Fuzzy Logic9
- Myths about Computational Intelligence10
- Computational Intelligence Application Areas11
- Summary14
- Exercises14
- Chapter 2. Computational Intelligence17
- Adaptation18
- Self-organization and Evolution26
- Historical Views of Computational Intelligence29
- Computational Intelligence as Adaptation and Self-organization30
- The Ability to Generalize34
- Computational Intelligence and Soft Computing versus Artificial Intelligence and Hard Computing35
- Summary36
- Exercises38
- Chapter 3. Evolutionary Computation Concepts and Paradigms39
- History of Evolutionary Computation40
- Evolutionary Computation Overview47
- Genetic Algorithms51
- Evolutionary Programming68
- Evolution Strategies75
- Genetic Programming81
- Particle Swarm Optimization87
- Summary92
- Exercises93
- Chapter 4. Evolutionary Computation Implementations95
- Implementation Issues97
- Genetic Algorithm Implementation103
- Particle Swarm Optimization Implementation118
- Summary142
- Exercises142
- Chapter 5. Neural Network Concepts and Paradigms145
- Neural Network History146
- What Neural Networks are and Why They are Useful165
- Neural Network Components and Terminology168
- Neural Network Topologies176
- Neural Network Adaptation179
- Comparing Neural Networks and Other Information Processing Methods188
- Preprocessing190
- Postprocessing195
- Summary196
- Exercises196
- Chapter 6. Neural Network Implementations197
- Implementation Issues198
- Back-propagation Implementation218
- The Kohonen Network Implementations235
- Evolutionary Back-propagation Network Implementation262
- Summary265
- Exercises265
- Chapter 7. Fuzzy Systems Conceptsand Paradigms269
- History270
- Fuzzy Sets and Fuzzy Logic275
- The Theory of Fuzzy Sets277
- Approximate Reasoning283
- Developing a Fuzzy Controller301
- Summary313
- Exercises314
- Chapter 8. Fuzzy Systems Implementations315
- Implementation Issues316
- Fuzzy Rule System Implementation320
- Evolving Fuzzy Rule Systems353
- Summary371
- Exercises371
- Chapter 9. Computational Intelligence Implementations373
- Implementation Issues374
- Fuzzy Evolutionary Fuzzy Rule System Implementation378
- Choosing the Best Tools382
- Applying Computational Intelligence to Data Mining385
- Summary387
- Exercises388
- Chapter 10. Performance Metrics389
- General Issues390
- Percent Correct395
- Average Sum-squared Error396
- Absolute Error398
- Normalized Error399
- Evolutionary Algorithm Effectiveness Metrics400
- Mann–Whitney U Test401
- Receiver Operating Characteristic Curves404
- Recall and Precision408
- Other ROC-related Measures409
- Confusion Matrices410
- Chi-square Test414
- Summary417
- Exercises417
- Chapter 11. Analysis and Explanation421
- Sensitivity Analysis422
- Hinton Diagrams427
- Computational Intelligence Tools for Explanation Facilities429
- Summary437
- Exercises438
- Bibliography439
- Index455
- About the Authors469
- Chapter 12. Case Study Summaries471
- Case Study Preview472
- Case Study 1: Detection of Electroencephalogram Spikes474
- Case Study 2: Determining Battery State of Charge481
- Case Study 3: Schedule Optimization484
- Case Study 4: Control System Design500
- Summary507
- Exercises507
- Glossary509
- Vendor Elsevier S & T
- SKU 9781558607590R150
- ISBN-13 9780080553832
- Author Eberhart, Russell C.; Shi, Yuhui
- Category Computers
- Subject Neural Networks
Do you have questions about this book?
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' methodologies
- Explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation
- Details the metrics and analytical tools needed to assess the performance of computational intelligence tools
- Concludes with a series of case studies that illustrate a wide range of successful applications
- Presents 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-study
- Makes available, on a companion website, a number of software implementations that can be adapted for real-world applications
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