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Adaptive Control with Recurrent High-order Neural Networks

Theory and Industrial Applications

  • George A. Rovithakis
  • Manolis A. Christodoulou

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

Table of contents

  1. Front Matter
    Pages I-XII
  2. George A. Rovithakis, Manolis A. Christodoulou
    Pages 1-8
  3. George A. Rovithakis, Manolis A. Christodoulou
    Pages 9-28
  4. George A. Rovithakis, Manolis A. Christodoulou
    Pages 29-51
  5. George A. Rovithakis, Manolis A. Christodoulou
    Pages 53-135
  6. George A. Rovithakis, Manolis A. Christodoulou
    Pages 137-164
  7. George A. Rovithakis, Manolis A. Christodoulou
    Pages 165-183
  8. Back Matter
    Pages 185-194

About this book

Introduction

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 offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Neural networks is one of those areas where an initial burst of enthusiasm and optimism leads to an explosion of papers in the journals and many presentations at conferences but it is only in the last decade that significant theoretical work on stability, convergence and robustness for the use of neural networks in control systems has been tackled. George Rovithakis and Manolis Christodoulou have been interested in these theoretical problems and in the practical aspects of neural network applications to industrial problems. This very welcome addition to the Advances in Industrial Control series provides a succinct report of their research. The neural network model at the core of their work is the Recurrent High Order Neural Network (RHONN) and a complete theoretical and simulation development is presented. Different readers will find different aspects of the development of interest. The last chapter of the monograph discusses the problem of manufacturing or production process scheduling.

Keywords

Non-linear Control Notation Scheduling Tracking adaptive control algorithms artificial intelligence control model modeling neural networks simulation stability uncertainty

Authors and affiliations

  • George A. Rovithakis
    • 1
  • Manolis A. Christodoulou
    • 1
  1. 1.Department of Electronic and Computer EngineeringTechnical University of CreteChania, CreteGreece

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-0785-9
  • Copyright Information Springer-Verlag London 2000
  • Publisher Name Springer, London
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4471-1201-3
  • Online ISBN 978-1-4471-0785-9
  • Series Print ISSN 1430-9491
  • Series Online ISSN 2193-1577
  • Buy this book on publisher's site
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