Intelligent Control Systems using Computational Intelligence Techniques
Published on 1. July 2005
Book
Hardback
476 pages
978-0-86341-489-3 (ISBN)
Description
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.
Reviews / Votes
'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 *More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 236 mm
Width: 157 mm
Thickness: 30 mm
Weight
816 gr
ISBN-13
978-0-86341-489-3 (9780863414893)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
01/2009
1st Edition
Institution of Engineering and Technology
€163.19
Available for download
Person
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 Wales in 1992. In 1992 he joined the Department of Electronic Engineering and Informatics of the University of 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.
Content
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
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