Intelligent Control Systems Using Computational Intelligence Techniques

✍ Scribed by Antonio Ruano

• Publisher: Institution of Engineering and Technology
• Year: 2005
• Tongue: English
• Leaves: 478
• Series: IEE Control Series
• Category: Library

✦ Synopsis

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.

Also available:

Optimal Relay and Saturating Control System Synthesis - ISBN 0906048567 Polynomial Methods in Optimal Control and Filtering - ISBN 0863412955

The Institution of Engineering and Technology is one of the world's leading professional societies for the engineering and technology community. The IET publishes more than 100 new titles every year; a rich mix of books, journals and magazines with a back catalogue of more than 350 books in 18 different subject areas including:

-Power & Energy -Renewable Energy -Radar, Sonar & Navigation -Electromagnetics -Electrical Measurement -History of Technology -Technology Management

✦ Table of Contents

Contents......Page 8
Preface......Page 16
Contributors......Page 20
1 An overview of nonlinear identification and control with fuzzy systems......Page 24
2 An overview of nonlinear identification and control with neural networks......Page 60
3 Multi-objective evolutionary computing solutions for control and system identification......Page 112
4 Adaptive local linear modelling and control of nonlinear dynamical systems......Page 142
5 Nonlinear system identification with local linear neuro-fuzzy models......Page 176
6 Gaussian process approaches to nonlinear modelling for control......Page 200
7 Neuro-fuzzy model construction, design and estimation......Page 242
8 A neural network approach for nearly optimal control of constrained nonlinear systems......Page 276
9 Reinforcement learning for online control and optimisation......Page 316
10 Reinforcement learning and multi-agent control within an internet environment......Page 350
11 Combined computational intelligence and analytical methods in fault diagnosis......Page 372
12 Application of intelligent control to autonomous search of parking place and parking of vehicles......Page 416
13 Applications of intelligent control in medicine......Page 438
Index......Page 470