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The purpose of this monograph is to give the broad aspects of nonlinear identification and control using neural networks. It consists of three parts:- an introduction to the fundamental principles of neural networks;- several methods for nonlinear identification using neural networks;- various techniques for nonlinear control using neural networks.A number of simulated and industrial examples are used throughout the monograph to demonstrate the operation of nonlinear identification and control techniques using neural networks. It should be emphasised that the methods and systems of nonlinear control have not progressed as rapidly as those for linear control. Comparatively speaking, at the present time, they are still in the development stage. We believe that the fundamental theory, various design methods and techniques, and several applications of nonlinear identification and control using neural networks that are presented in this monograph will enable the reader to analyse and synthesise nonlinear control systems quantitatively.
Helicopters, aircraft and missiles are just some of the practical
multivariable control systems to which eigenstructure assignment
has been applied in recent years. Liu and Patton offer a uniquely
integrated introduction to eigenstructure assignment theory and
techniques for multi-input multi-output control system design.
Features include:
The purpose of this monograph is to give the broad aspects of nonlinear identification and control using neural networks. It uses a number of simulated and industrial examples throughout, to demonstrate the operation of nonlinear identification and control techniques using neural networks.
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