Computational Intelligence (eBook)
496 Seiten
Elsevier Science (Verlag)
978-0-08-055383-2 (ISBN)
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
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.
Computational Intelligence: Concepts to Implementations provides the most complete and practical coverage of computational intelligence tools and techniques to date. This book integrates various natural and engineering disciplines to establish Computational Intelligence. This is the first comprehensive textbook on the subject, supported with lots of practical examples. It 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. This book lays emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. 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. The book moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific con. It explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation. It details the metrics and analytical tools needed to assess the performance of computational intelligence tools. The book concludes with a series of case studies that illustrate a wide range of successful applications. This book will appeal to professional and academic researchers in computational intelligence applications, tool development, and systems. - 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
Front Cover 1
Computational Intelligence 4
Copyright Page 5
Table of Contents 6
Preface 14
Chapter 1. Foundations 22
Definitions 23
Biological Basis for Neural Networks 25
Behavioral Motivations for Fuzzy Logic 30
Myths about Computational Intelligence 31
Computational Intelligence Application Areas 32
Summary 35
Exercises 35
Chapter 2. Computational Intelligence 38
Adaptation 39
Self-organization and Evolution 47
Historical Views of Computational Intelligence 50
Computational Intelligence as Adaptation and Self-organization 51
The Ability to Generalize 55
Computational Intelligence and Soft Computing versus Artificial Intelligence and Hard Computing 56
Summary 57
Exercises 59
Chapter 3. Evolutionary Computation Concepts and Paradigms 60
History of Evolutionary Computation 61
Evolutionary Computation Overview 68
Genetic Algorithms 72
Evolutionary Programming 89
Evolution Strategies 96
Genetic Programming 102
Particle Swarm Optimization 108
Summary 113
Exercises 114
Chapter 4. Evolutionary Computation Implementations 116
Implementation Issues 118
Genetic Algorithm Implementation 124
Particle Swarm Optimization Implementation 139
Summary 163
Exercises 163
Chapter 5. Neural Network Concepts and Paradigms 166
Neural Network History 167
What Neural Networks are and Why They are Useful 186
Neural Network Components and Terminology 189
Neural Network Topologies 197
Neural Network Adaptation 200
Comparing Neural Networks and Other Information Processing Methods 209
Preprocessing 211
Postprocessing 216
Summary 217
Exercises 217
Chapter 6. Neural Network Implementations 218
Implementation Issues 219
Back-propagation Implementation 239
The Kohonen Network Implementations 256
Evolutionary Back-propagation Network Implementation 283
Summary 286
Exercises 286
Chapter 7. Fuzzy Systems Conceptsand Paradigms 290
History 291
Fuzzy Sets and Fuzzy Logic 296
The Theory of Fuzzy Sets 298
Approximate Reasoning 304
Developing a Fuzzy Controller 322
Summary 334
Exercises 335
Chapter 8. Fuzzy Systems Implementations 336
Implementation Issues 337
Fuzzy Rule System Implementation 341
Evolving Fuzzy Rule Systems 374
Summary 392
Exercises 392
Chapter 9. Computational Intelligence Implementations 394
Implementation Issues 395
Fuzzy Evolutionary Fuzzy Rule System Implementation 399
Choosing the Best Tools 403
Applying Computational Intelligence to Data Mining 406
Summary 408
Exercises 409
Chapter 10. Performance Metrics 410
General Issues 411
Percent Correct 416
Average Sum-squared Error 417
Absolute Error 419
Normalized Error 420
Evolutionary Algorithm Effectiveness Metrics 421
Mann–Whitney U Test 422
Receiver Operating Characteristic Curves 425
Recall and Precision 429
Other ROC-related Measures 430
Confusion Matrices 431
Chi-square Test 435
Summary 438
Exercises 438
Chapter 11. Analysis and Explanation 442
Sensitivity Analysis 443
Hinton Diagrams 448
Computational Intelligence Tools for Explanation Facilities 450
Summary 458
Exercises 459
Bibliography 460
Index 476
About the Authors 490
Chapter 12. Case Study Summaries 492
Case Study Preview 493
Case Study 1: Detection of Electroencephalogram Spikes 495
Case Study 2: Determining Battery State of Charge 502
Case Study 3: Schedule Optimization 505
Case Study 4: Control System Design 521
Summary 528
Exercises 528
Glossary 530
| Erscheint lt. Verlag | 18.4.2011 |
|---|---|
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Netzwerke |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 0-08-055383-4 / 0080553834 |
| ISBN-13 | 978-0-08-055383-2 / 9780080553832 |
| Haben Sie eine Frage zum Produkt? |
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