Handbook on Computational Intelligence, In 2 Volumes

With the Internet, the proliferation of Big Data, and autonomous systems, mankind has entered into an era of ‘digital obesity’. In this century, computational intelligence, such as thinking machines, have been brought forth to process complex human problems in a wide scope of areas — from social sciences, economics and biology, medicine and social networks, to cyber security.

The Handbook of Computational Intelligence prompts the readers to look at these problems from a non-traditional angle. It takes a step by step approach, supported by case studies, to explore the issues that have arisen in the process. The Handbook covers many classic paradigms, as well as recent achievements and future promising developments to solve some of these very complex problems.

This book is a “one-stop-shop” for beginners, as well as an inspirational source for more advanced researchers. It is a useful resource for lecturers and learners alike.

Table of Contents

Volume 1: Fuzzy Logic, Systems, Artificial Neural Networks, and Learning Systems

Part I: Fuzzy Logic and Systems
Chapter 1. Fundamentals of Fuzzy Set Theory
Chapter 2. Granular Computing
Chapter 3. Evolving Fuzzy Systems—Fundamentals, Reliability, Interpretability, Useability, Applications
Chapter 4. Modeling Fuzzy Rule-Based Systems
Chapter 5. Fuzzy Classifiers
Chapter 6. Fuzzy Model-Based Control—Predictive and Adaptive Approaches
Chapter 7. Fuzzy Fault Detection and Diagnosis

Part II: Artificial Neural Networks and Learning Systems
Chapter 8. The ANN and Learning Systems in Brains and Machines
Chapter 9. Introduction to Cognitive Systems
Chapter 10. A New View on Economics with Recurrent Neural Networks
Chapter 11. Evolving Connectionist Systems for Adaptive Learning and Pattern Recognition: From Neuro-Fuzzy-, to Spiking- and Neurogenetic
Chapter 12. Reinforcement Learning with Applications in Automation Decision and Feedback Control
Chapter 13. Kernel Models and Support Vector Machines

Volume 2: Evolutionary Computation, Hybrid Systems, and Applications

Part III: Evolutionary Computation
Chapter 14. History and Philosophy of Evolutionary Computation
Chapter 15. A Survey of Recent Works in Artificial Immune Systems
Chapter 16. Swarm Intelligence: An Introduction, History and Applications
Chapter 17. Memetic Algorithms

Part IV: Hybrid Systems
Chapter 18. Multi-Objective Evolutionary Design of Fuzzy Rule-Based Systems
Chapter 19. Bio-Inspired Optimization of Interval Type-2 Fuzzy Controllers
Chapter 20. Nature-Inspired Optimization of Fuzzy Controllers and Fuzzy Models
Chapter 21. Genetic Optimization of Modular Neural Networks for Pattern Recognition with a Granular Approach
Chapter 22. Hybrid Evolutionary-, Constructive- and Evolving Fuzzy Neural Networks

Part V: Applications
Chapter 23. Applications of Computational Intelligence to Decision-Making: Modeling Human Reasoning/Agreement
Chapter 24. Applications of Computational Intelligence to Process Industry
Chapter 25. Applications of Computational Intelligence to Robotics and Autonomous Systems
Chapter 26. Selected Automotive Applications of Computational Intelligence