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Neural Networks for Identification, Prediction and Control

  • Book
  • © 1995

Overview

Authors:
  1. Duc Truong Pham

    1. Intelligent Systems Laboratory Systems Division, Cardiff School of Engineering, University of Wales, Cardiff, UK

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  2. Xing Liu

    1. Intelligent Systems Laboratory Systems Division, Cardiff School of Engineering, University of Wales, Cardiff, UK

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    You can also search for this author in PubMed   Google Scholar

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Table of contents (8 chapters)

  1. Front Matter

    Pages I-xiv
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  2. Artificial Neural Networks

    • Duc Truong Pham, Xing Liu
    Pages 1-23

  3. Dynamic System Identification Using Feedforward Neural Networks

    • Duc Truong Pham, Xing Liu
    Pages 25-46

  4. Dynamic System Identification Using Recurrent Neural Networks

    • Duc Truong Pham, Xing Liu
    Pages 47-61

  5. Modelling and Prediction Using GMDH Networks

    • Duc Truong Pham, Xing Liu
    Pages 63-82

  6. Financial Prediction Using Neural Networks

    • Duc Truong Pham, Xing Liu
    Pages 83-110

  7. Neural Network Controllers

    • Duc Truong Pham, Xing Liu
    Pages 111-130

  8. Neuromorphic Fuzzy Controller Design

    • Duc Truong Pham, Xing Liu
    Pages 131-142

  9. Robot Manipulator Control Using Neural Networks

    • Duc Truong Pham, Xing Liu
    Pages 143-165

  10. Back Matter

    Pages 167-238
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Keywords

About this book

In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modelling), prediction and control. Neural networks are computing systems characterised by the ability to learn from examples rather than having to be programmed in a conventional sense. Their use enables the behaviour of complex systems to be modelled and predicted and accurate control to be achieved through training, without a priori information about the systems' structures or parameters. This book describes examples of applications of neural networks In modelling, prediction and control. The topics covered include identification of general linear and non-linear processes, forecasting of river levels, stock market prices and currency exchange rates, and control of a time-delayed plant and a two-joint robot. These applications employ the major types of neural networks and learning algorithms. The neural network types considered in detail are the muhilayer perceptron (MLP), the Elman and Jordan networks and the Group-Method-of-Data-Handling (GMDH) network. In addition, cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems are also presented. The main learning algorithm adopted in the applications is the standard backpropagation (BP) algorithm. Widrow-Hoff learning, dynamic BP and evolutionary learning are also described.

Authors and Affiliations

  • Intelligent Systems Laboratory Systems Division, Cardiff School of Engineering, University of Wales, Cardiff, UK

    Duc Truong Pham, Xing Liu

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