Nonlinear Identification and Control: A Neural Network Approach

✍ Scribed by G. P. Liu BEng, MEng, PhD (auth.)

Publisher: Springer-Verlag London
Year: 2001
Tongue: English
Leaves: 223
Series: Advances in Industrial Control
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies . . . , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series otTers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The time for nonlinear control to enter routine application seems to be approaching. Nonlinear control has had a long gestation period but much ofthe past has been concerned with methods that involve formal nonlinear functional model representations. It seems more likely that the breakthough will come through the use of other more flexible and amenable nonlinear system modelling tools. This Advances in Industrial Control monograph by Guoping Liu gives an excellent introduction to the type of new nonlinear system modelling methods currently being developed and used. Neural networks appear prominent in these new modelling directions. The monograph presents a systematic development of this exciting subject. It opens with a useful tutorial introductory chapter on the various tools to be used. In subsequent chapters Doctor Liu leads the reader through identification, and then onto nonlinear control using nonlinear system neural network representations.

✦ Table of Contents

Front Matter....Pages i-xx
Neural Networks....Pages 1-25
Sequential Nonlinear Identification....Pages 27-52
Recursive Nonlinear Identification....Pages 53-76
Multiobjective Nonlinear Identification....Pages 77-100
Wavelet Based Nonlinear Identification....Pages 101-124
Nonlinear Adaptive Neural Control....Pages 125-141
Nonlinear Predictive Neural Control....Pages 143-161
Variable Structure Neural Control....Pages 163-178
Neural Control Application to Combustion Processes....Pages 179-192
Back Matter....Pages 193-210

✦ Subjects

Dynamical Systems and Ergodic Theory;Control;Artificial Intelligence (incl. Robotics);Complexity

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