Scope: Intelligent Control techniques are becoming important tools in both academia and industry. Methodologies developed in the field of soft-computing, such as neural networks, fuzzy systems and evolutionary computation, can lead to accommodation of more complex processes, improved performance and considerable time savings and cost reductions. Intelligent Control Systems Using Computational Intelligence Techniques details the application of these tools to the field of control systems.
Each chapter gives an overview of current approaches in the topic covered, with a set of the most important set references in the field, and then details the author’s approach, examining both the theory and practical applications.
Antonio Ruano received his First Degree in Electronic and Telecommunications Engineering from the University of Aveiro, Portugal, in 1982, his MSc in Electrothecnic Engineering from the University of Coimbra, Portugal, in 1989, and his PhD degree in Electronic Engineering from the University of Wales in 1992. In 1992 he joined the Department of Systems Engineering and Computing of the Faculty of Sciences & Technology of the Universidade do Algarve, where in 1996 he became Associate Professor of Automatic Control.
He is Associate Editor for Automatica, a member of the Editorial Board of International Journal of Systems Science, and serves as reviewer for several journals and international Conferences.
He is a senior member of the IEEE and a member of the Cognition for Control, Real-Time Computing and Control and Computer Control for Agricultural Applications TCs of IFA.
"...a very comprehensive and up-to-date treatise of this important and fascinating field. The book is an excellent read for anyone (academic or industrialist) who wishes to discover how to utilise computational intelligence to solve realistic problems. It is an extremely interesting and fascinating book on this dynamic subject, I certainly found it an enthralling read and it has been given a prominent place on my bookshelf." Dr. Karl O. Jones, Measurement + Control, Vol.40, No.2, March 2007
This book is aimed at Postgraduate students and Industry Researchers in the areas of Control and Computational Intelligence
An Overview of Nonlinear Identification and Control with Fuzzy Systems. An Overview of Nonlinear Identification and Control with Neural Networks. Multiobjective Evolutionary Computing Solutions for Control and System Identification. Adaptive Local Linear Modelling and Control of Nonlinear Dynamical Systems.
Nonlinear System Identification with Local Linear Neuro-Fuzzy Models. Gaussian Process Approaches to Nonlinear Modelling for Control. Neuro Fuzzy Model Construction, Design and Estimation. A Neural Network Approach for nearly Optimal Control of Constrained Nonlinear Systems. Reinforcement Learning for Online Control and Optimisation. Reinforcement Learning and Multiagent Control Within An Internet Environment.
Combined Computational Intelligence and Analytical Methods in Fault Diagnosis. Application of Intelligent Control to Autonomous Search of Parking Place and Parking of Vehicles. Applications of Intelligent Control in Medicine.