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Description
The Handbook on Computational Intelligence aims to be a one-stop-shop for the various aspects of the broad research area of Computational Intelligence. The Handbook is organized into five parts over two volumes:
(1) Fuzzy Sets and Systems (Vol. 1)
(2) Artificial Neural Networks and Learning Systems (Vol. 1)
(3) Evolutionary Computation (Vol. 2)
(4) Hybrid Systems (Vol. 2)
(5) Applications (Vol. 2)
In total, 26 chapters detail various aspects of the theory, methodology and applications of Computational Intelligence. The authors of the different chapters are leading researchers in their respective fields or “rising stars” (promising early career researchers). This mix of experience and energy provides an invaluable source of information that is easy to read, rich in detail, and wide in spectrum. In total, over 50 authors from 16 different countries including USA, UK, Japan, Germany, Canada, Italy, Spain, Austria, Bulgaria, Brazil, Russia, India, New Zealand, Hungary, Slovenia, Mexico, and Romania contributed to this collaborative effort. The scope of the Handbook covers practically all important aspects of the topic of Computational Intelligence and has several chapters dedicated to particular applications written by leading industry-based or industry-linked researchers.
Preparing, compiling and editing this Handbook was an enjoyable and inspirational experience. I hope you will also enjoy reading it and will find answers to your questions and will use this book in your everyday work.
Content:-
Introduction by the Editor
About the Editor
Acknowledgments
Prologue
Volume 1: Fuzzy Logic, Systems, Artificial Neural Networks, and Learning Systems
Part I: Fuzzy Logic and Systems
1. Fundamentals of Fuzzy Set Theory
About the Editor
Acknowledgments
Prologue
Volume 1: Fuzzy Logic, Systems, Artificial Neural Networks, and Learning Systems
Part I: Fuzzy Logic and Systems
1. Fundamentals of Fuzzy Set Theory
2. Granular Computing
3. Evolving Fuzzy Systems — Fundamentals, Reliability, Interpretability, Useability, Applications
4. Modeling Fuzzy Rule-Based Systems
5. Fuzzy Classifiers
6. Fuzzy Model-Based Control — Predictive and Adaptive Approaches
7. Fuzzy Fault Detection and Diagnosis
Part II: Artificial Neural Networks and Learning Systems8. The ANN and Learning Systems in Brains and Machines
9. Introduction to Cognitive Systems
10. A New View on Economics with Recurrent Neural Networks
11. Evolving Connectionist Systems for Adaptive Learning and Pattern Recognition: From
12. Reinforcement Learning with Applications in Automation Decision and Feedback Control
13. Kernel Models and Support Vector Machines
Volume 2: Evolutionary Computation, Hybrid Systems, and Applications
Part III: Evolutionary Computation14. History and Philosophy of Evolutionary Computation
Part III: Evolutionary Computation14. History and Philosophy of Evolutionary Computation
15. A Survey of Recent Works in Artificial Immune Systems
16. Swarm Intelligence: An Introduction, History and Applications
17. Memetic Algorithms
Part IV: Hybrid Systems
18. Multi-Objective Evolutionary Design of Fuzzy Rule-Based Systems
18. Multi-Objective Evolutionary Design of Fuzzy Rule-Based Systems
19. Bio-Inspired Optimization of Interval Type-2 Fuzzy Controllers
20. Nature-Inspired Optimization of Fuzzy Controllers and Fuzzy Models
21. Genetic Optimization of Modular Neural Networks for Pattern Recognition with a Granular Approach
22. Hybrid Evolutionary-, Constructive- and Evolving Fuzzy Neural Networks
Part V: Applications
23. Applications of Computational Intelligence to Decision-Making: Modeling Human Reasoning/Agreement
23. Applications of Computational Intelligence to Decision-Making: Modeling Human Reasoning/Agreement
24. Applications of Computational Intelligence to Process Industry
25. Applications of Computational Intelligence to Robotics and Autonomous Systems
26. Selected Automotive Applications of Computational Intelligence
Index
Author Details
"Professor Plamen Angelov" holds a Personal Chair in Intelligent Systems and leads the Data Science Group at Lancaster University, UK. He has PhD (1993) and Doctor of Sciences (DSc, 2015) degrees and is a Fellow of both the IEEE and IET, as well as a Senior Member of the International Neural Networks Society (INNS). He is also a member of the Boards of Governors of both bodies for the period 2014–2017. He also chairs the Technical Committee (TC) on Evolving Intelligent Systems
within the Systems, Man and Cybernetics Society, IEEE and is a member of the TCs on Neural Networks and on Fuzzy Systems within the Computational Intelligence Society, IEEE.
within the Systems, Man and Cybernetics Society, IEEE and is a member of the TCs on Neural Networks and on Fuzzy Systems within the Computational Intelligence Society, IEEE.
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