Nonlinear Identification and Control

A Neural Network Approach

  • G. P. Liu

Part of the Advances in Industrial Control book series (AIC)

Table of contents

  1. Front Matter
    Pages i-xx
  2. G. P. Liu
    Pages 1-25
  3. G. P. Liu
    Pages 27-52
  4. G. P. Liu
    Pages 53-76
  5. G. P. Liu
    Pages 101-124
  6. G. P. Liu
    Pages 125-141
  7. G. P. Liu
    Pages 143-161
  8. G. P. Liu
    Pages 163-178
  9. Back Matter
    Pages 193-210

About this book


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.


Adaptive control Control Control Engineering Identification Modelling Neural Networks Nonlinear control Wavelets artificial intelligence development genetic algorithms learning model

Authors and affiliations

  • G. P. Liu
    • 1
  1. 1.School of Mechanical Materials, Manufacturing Engineering and ManagementUniversity of NottinghamNottinghamUK

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag London Limited 2001
  • Publisher Name Springer, London
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4471-1076-7
  • Online ISBN 978-1-4471-0345-5
  • Series Print ISSN 1430-9491
  • Series Online ISSN 2193-1577
  • Buy this book on publisher's site