Book description
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.
Table of contents
- Cover
- Title Page
- Copyright
- Dedication
- Foreword
- Preface
- Acknowledgements
-
Chapter 1: Introduction to Computational Intelligence
- 1.1 Computational Intelligence
- 1.2 Paradigms of Computational Intelligence
- 1.3 Approaches to Computational Intelligence
- 1.4 Synergies of Computational Intelligence Techniques
- 1.5 Applications of Computational Intelligence
- 1.6 Grand Challenges of Computational Intelligence
- 1.7 Overview of the Book
- 1.8 MATLAB® Basics
- References
-
Chapter 2: Introduction to Fuzzy Logic
- 2.1 Introduction
- 2.2 Fuzzy Logic
- 2.3 Fuzzy Sets
- 2.4 Membership Functions
- 2.5 Features of MFs
- 2.6 Operations on Fuzzy Sets
- 2.7 Linguistic Variables
- 2.8 Linguistic Hedges
- 2.9 Fuzzy Relations
- 2.10 Fuzzy If–Then Rules
- 2.11 Fuzzification
- 2.12 Defuzzification
- 2.13 Inference Mechanism
- 2.14 Worked Examples
- 2.15 MATLAB® Programs
- References
- Chapter 3: Fuzzy Systems and Applications
- Chapter 4: Neural Networks
- Chapter 5: Neural Systems and Applications
- Chapter 6: Evolutionary Computing
- Chapter 7: Evolutionary Systems
- Chapter 8: Evolutionary Fuzzy Systems
- Chapter 9: Evolutionary Neural Networks
- Chapter 10: Neural Fuzzy Systems
- Appendix A: MATLAB® Basics
- Appendix B: MATLAB® Programs for Fuzzy Logic
- Appendix C: MATLAB® Programs for Fuzzy Systems
- Appendix D: MATLAB® Programs for Neural Systems
- Appendix E: MATLAB® Programs for Neural Control Design
- Appendix F: MATLAB® Programs for Evolutionary Algorithms
- Appendix G: MATLAB® Programs for Neuro-Fuzzy Systems
- Index
Product information
- Title: Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing
- Author(s):
- Release date: June 2013
- Publisher(s): Wiley
- ISBN: 9781118337844
You might also like
book
Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration
Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, …
book
Computational Intelligence
Computational Intelligence: Concepts to Implementations provides the most complete and practical coverage of computational intelligence tools …
book
Strengthening Deep Neural Networks
As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" …
book
Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner …