Intelligent Control Systems using Computational Intelligence Techniques

by A.E. Ruano

Overview

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 Intellingence Techniques details the application of these tools to the field of control systems. Each chapter gives and overview of current approaches in the topic covered, with a set of the most important references in the field, and then details the author's approach, examining both the theory and practical applications.

Product Details

ISBN-13:	9780863414893
Publisher:	The Institution of Engineering and Technology
Publication date:	07/18/2005
Series:	Control, Robotics and Sensors , #70
Pages:	476
Product dimensions:	6.40(w) x 9.30(h) x 1.20(d)

About the Author

Antonio Ruano received his First Degree in Electronic and Telecommunications Engineering from the Universityof Aveiro, Portugal, in 1982, his MSc in Electrothecnic Engineering from the Universityof Wales in 1992. In 1992 he joined the Department of Electronic Engineering and Informatics of the Universityof 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 a reviewer for several journals and international conferences. He is a senior member of the IEE and a member of the Cognition for Control, Real-Time Computing and Control and Computer Control for Agricultural Applications TCs of IFAC.

Table of Contents

• Chapter 1: An overview of nonlinear identification and control with fuzzy systems
• Chapter 2: An overview of nonlinear identification and control with neural networks
• Chapter 3: Multi-objective evolutionary computing solutions for control and system identification
• Chapter 4: Adaptive local linear modelling and control of nonlinear dynamical systems
• Chapter 5: Nonlinear system identification with local linear neuro-fuzzy models
• Chapter 6: Gaussian process approaches to nonlinear modelling for control
• Chapter 7: Neuro-fuzzy model construction, design and estimation
• Chapter 8: A neural network approach for nearly optimal control of constrained nonlinear systems
• Chapter 9: Reinforcement learning for online control and optimisation
• Chapter 10: Reinforcement learning and multi-agent control within an internet environment
• Chapter 11: Combined computational intelligence and analytical methods in fault diagnosis
• Chapter 12: Application of intelligent control to autonomous search of parking place and parking of vehicles
• Chapter 13: Applications of intelligent control in medicine

Editorial Reviews

'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.'

Measurement + Control - Dr. Karl O. Jones