Angelov P.P. (ed.) Handbook on Computational Intelligence

Singapore: World Scientific, 2016. - 957 p.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.- Fundamentals of Fuzzy Set Theory
This chapter sets the tone with a thorough, step by step introduction to the theory of fuzzy sets. It is written by one of the foremost experts in this area, Dr. Fernando Gomide, Professor at University of Campinas, Campinas, Brazil.
- Granular Computing
Granular computing became a cornerstone of Computational Intelligence and the chapter offers a thorough review of the problems and solutions. It is written by two of the leading experts in this area, Dr. Andrzej Bargiela, Professor at Nottingham University, UK (now in Christchurch University, New Zealand) and Dr. Witold Pedrycz, Professor at University of Alberta, Canada.
- Evolving Fuzzy Systems — Fundamentals, Reliability, Interpretability, Useability, Applications Since its introduction around the turn of the centuries by the Editor, the area of evolving fuzzy systems is constantly developing and this chapter offers a review of the problems
and some of the solutions. It is written by Dr. Edwin Lughofer, Key Researcher at Johannes Kepler University, Linz, Austria, who quickly became one of the leading experts in this area following an exchange of research visits with Lancaster University, UK.
- Modeling of Fuzzy Rule-based Systems This chapter covers the important problem of designing fuzzy systems from data. It is written by Dr. Rashmi Dutta Baruah of Indian Institute of Technology, Guwahati, India and Diganta Baruah of Sikkim Manipal Institute of Technology, Sikkim, India. Rashmi recently obtained a Ph.D. degree from Lancaster University, UK in the area of evolving fuzzy systems.
- Fuzzy Classifiers
This chapter covers the very important problem of fuzzy rule-based classifiers and is written by the expert in the field, Dr. Hamid Bouchachia, Associate Professor at Bournemouth University, UK.
- Fuzzy Model based Control: Predictive and Adaptive Approach
This chapter covers the problems of fuzzy control and is written by the experts in the area, Dr. Igor Škrjanc, Professor and Dr. Sašo Blažič, Professor at the University of Ljubljana, Slovienia.
- Fuzzy Fault Detection and Diagnosis
This chapter is written by Dr. Bruno Costa, Professor at IFRN, Natal, Brazil who specialized recently in Lancaster, UK.
- The ANN and Learning Systems in Brains and Machines
This chapter is written by Dr. Leonid Perlovsky from Harvard University, USA.
- Introduction to Cognitive Systems
This chapter is written by Dr. Peter Erdi, Professor at Kalamazoo College, USA, coauthored by Dr. Mihaly Banyai (leading author) from Wigner RCP, Hungarian Academy of Sciences.
- A New View on Economics with Recurrent Neural Networks
This chapter offers a rather specific view on the recurrent neural networks from the point of view of their importance for modeling economic processes and is written by a team of industry-based researchers from Siemens, Germany including Drs. Hans Georg Zimmermann, Ralph Grothmann and Christoph Tietz.
- Evolving Connectionist Systems for Adaptive Learning and Pattern Recognition: From Neuro-Fuzzy, to Spiking and Neurogenetic
This chapter offers a review of one of the cornerstones of Computational Intelligence, namely, the evolving connectionist systems, and is written by the pioneer in this area Dr. Nikola Kasabov, Professor at Auckland University of Technology, New Zealand.
- Reinforcement Learning with Applications in Automation Decision and Feedback Control
This chapter offers a thorough and advanced analysis of the reinforcement learning from the perspective of decision-making and control. It is written by one of the world’s leading experts in this area, Dr. Frank L. Lewis, Professor at The University of Texas, co-authored by Dr. Kyriakos Vamvoudakis from the same University (the leading author) and Dr. Draguna Vrabie from the United Technologies Research Centre, USA.
- Kernel Models and Support Vector Machines
This chapter offers a very skilful review of one of the hottest topics in research and applications linked to classification and related problems. It is written by a team of young Russian researchers who are finishing their Ph.D. studies at Lancaster University, UK (Denis Kolev and Dmitry Kangin) and by Mikhail Suvorov. All three graduated from leading Moscow Universities (Moscow State University and Bauman Moscow State Technical University).
- History and Philosophy of the Evolutionary Computation
This chapter lays the basis for one of the pillars of Computational Intelligence, covering its history and basic principles. It is written by one of the well-known experts in this area, Dr. Carlos A. Coello-Coello from CINVESTAV, Mexico and co-authored by Dr. Carlos Segura from the Centre of Research in Mathematics, Mexico and Dr. Gara Miranda from the University of La Laguna, Tenerife, Spain.
- A Survey of Recent Works in Artificial Immune Systems
This chapter covers one of the important aspects of Computational Intelligence which is associated with the Evolutionary Computation. It is written by the pioneer in the area Dr. Dipankar Dasgupta, Professor at The University of Memphis, USA and is coauthored by Dr. Guilherme Costa Silva from the same University who is the leading author.
- Swarm Intelligence: An Introduction, History and Applications
This chapter covers another important aspect of Evolutionary Computation and is written by Dr. Fevrier Valdez from The Institute of Technology, Tijuana, Mexico.
- Memetic Algorithms
This chapter reviews another important type of methods and algorithms which are associated with the Evolutionary Computation and is written by a team of authors from the University of Aizu, Japan led by Dr. Qiangfu Zhao who is a well-known expert in the area of Computational Intelligence. The team also includes Drs. Yong Liu and Yan Pei.
- Multi-objective Evolutionary Design of Fuzzy Rule-Based Systems
This chapter covers one of the areas of hybridization where Evolutionary Computation is used as an optimization tool for automatic design of fuzzy rule-based systems from data. It is written by the well-known expert in this area, Dr. Francesco Marcelloni, Professor at the University of Pisa, Italy, supported by Dr. Michaela Antonelli and Dr. Pietro Ducange from the same University.
- Bio-inspired Optimization of Type-2 Fuzzy Controllers
The chapter offers a hybrid system where a fuzzy controller of the so-called type-2 is being optimized using a bio-inspired approach. It is written by one of the leading experts in type-2 fuzzy systems, Dr. Oscar Castillo, Professor at The Institute of Technology, Tijuana Mexico.
- Nature-inspired Optimization of Fuzzy Controllers and Fuzzy Models
This chapter also offers a hybrid system in which fuzzy models and controllers are being optimized using nature-inspired optimization methods. It is written by the well-known expert in the area of fuzzy control, Dr. Radu-Emil Precup, Professor at The Polytechnic University of Timisoara, Romania and co-authored by Dr. Radu Codrut David.
- Genetic Optimization of Modular Neural Networks for Pattern Recognition with a Granular Approach
This chapter describes a hybrid system whereby modular neural networks using a granular approach are optimized by a genetic algorithm and applied for pattern recognition. It is written by Dr. Patricia Melin, Professor at The Institute of Technology, Tijuana, Mexico who is well-known through her work in the area of hybrid systems.
- Hybrid Evolutionary-, Constructive-, and Evolving Fuzzy Neural Networks
This is another chapter by the pioneer of evolving neural networks, Professor Dr. Nikola Kasabov, co-authored by Dr. Michael Watts (leading author), both from Auckland, New Zealand.
- Applications of Computational Intelligence to Decision-Making: Modeling Human Reasoning/Agreement
This chapter covers the use of Computational Intelligence in decision-making applications, in particular, modeling human reasoning and agreement. It is authored by the leading expert in this field, Dr. Jonathan Garibaldi, Professor at The Nottingham University, UK and co-authored by Drs. Simon Miller (leading author) and Christian Wagner from the same University.
- Applications of Computational Intelligence to Process Industry
This chapter offers the industry-based researcher’s point of view. Dr. Jose Juan Macias Hernandez is leading a busy Department of Process Control at the largest oil refinery on the Canary Islands, Spain and is also Associate Professor at the local University of La Laguna, Tenerife.
- Applications of Computational Intelligence to Robotics and Autonomous Systems
This chapter describes applications of Computational Intelligence to the area of Robotics and Autonomous Systems and is written by Dr. Adham Atyabi and Professor Dr. Samia Nefti-Meziani, both from Salford University, UK.
- Selected Automotive Applications of Computational Intelligence
Last, but not least, the chapter by the pioneer of fuzzy systems area Dr. Dimitar Filev, co-authored by his colleagues, Dr. Mahmoud Abou-Nasr (leading author) and Dr. Fazal Sayed (all based at Ford Motors Co., Dearborn, MI, USA) offers the industry-based leaders’ point of view.