Neural Network Applications in Control

Neural Network Applications in Control

ISBN-10:
0852968523
ISBN-13:
9780852968529
Pub. Date:
06/30/1995
Publisher:
The Institution of Engineering and Technology
ISBN-10:
0852968523
ISBN-13:
9780852968529
Pub. Date:
06/30/1995
Publisher:
The Institution of Engineering and Technology
Neural Network Applications in Control

Neural Network Applications in Control

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Overview

Neural networks are an exciting technology of growing importance in real industrial situations, particularly in control and systems. This book aims to give a detailed appreciation of the use of neural nets in these applications; it is aimed particularly at those with a control or systems background who wish to gain an insight into the technology in the context of real applications.

The book introduces a wide variety of network types, including Kohenen nets, n-tuple nets and radial basis function networks, as well as the more usual multi-layer perception back-propagation networks. It begins by describing the basic principles and some essential design features, then goes on to examine in depth several application studies illustrating a range of advanced approaches to the topic.


Product Details

ISBN-13: 9780852968529
Publisher: The Institution of Engineering and Technology
Publication date: 06/30/1995
Series: Control, Robotics and Sensors
Pages: 309
Product dimensions: 6.40(w) x 9.30(h) x 0.90(d)

About the Author

George Irwin was appointed to a personal chair in control engineering at Queen's UniversityBelfast in 1989. His research interests include algorithms and architectures for real-time control, neural networks and control applications in aerospace, electric power and chemical plant. He has published extensively in the field and has won the lEE's Kelvin, Heaviside and Mather premiums. Prof. Irwin is a member of the international Editorial Board of the IEE Proceedings on Control Theory & Applications and an Associate Editor of Control Engineering Practice. He also serves on the executive committee of the UK Automatic Control Council and the IFAC Technical Committee on Algorithms and Architectures for Real-Time Control. He is a Chartered Engineer and a Fellow of the IEE.


Kevin Warwick is Professor of Cybernetics and Head of the School of Engineering and Information Sciences at the Universityof Reading. He has previously worked at Oxford University, Imperial College, Newcastle Universityand Warwick University, as well as being employed by British Telecom for six years. A Chartered Engineer, a Fellow of the IEE and an Honorary Editor of the IEE Proceedings, he has published well over 200 papers in the areas of computer control, intelligent systems and neural networks.


Ken Hunt is with Daimler-Benz Systems Technology Research Berlin. His research activities involve the application of neural network methods for the modelling and control of nonlinear dynamic systems in applications including automotive and industrial control problems. He previously worked in the Mechanical Engineering Department of Glasgow University, with a Royal Society of Edinburgh research fellowship. Dr Hunt is the author or editor of six books and has published over 60 journal and conference papers. He is a Chartered Engineer, a Member of the IEE and serves on the IEE Professional Group Committee on Applied Control Techniques.

Table of Contents

  • Chapter 1: Neural networks: an introduction
  • Chapter 2: Digital neural networks
  • Chapter 3: Fundamentals of neurocontrol: a survey
  • Chapter 4: Selection of neural network structures: some approximation theory guidelines
  • Chapter 5: Electric power and chemical process applications
  • Chapter 6: Studies in artificial neural network based control
  • Chapter 7: Applications of dynamic artificial neural networks in state estimation and nonlinear process control
  • Chapter 8: Speech, vision and colour applications
  • Chapter 9: Real-time drive control with neural networks
  • Chapter 10: Fuzzy-neural control in intensive-care blood pressure management
  • Chapter 11: Neural networks and system identification
  • Chapter 12: Neurofuzzy adaptive modelling and construction of nonlinear dynamical processes
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