© 1995

Applications of Neural Networks

  • Alan F. Murray

Table of contents

  1. Front Matter
    Pages i-xii
  2. Introduction

    1. Alan Murray
      Pages 1-33
  3. Supervised Training

    1. Pattern Recognition and Classification

      1. John M. Vincent
        Pages 35-70
      2. B. Golomb, T. Sejnowski
        Pages 71-92
    2. Diagnosis and Monitoring

      1. M. Jabri, S. Pickard, P. Leong, Z. Chi, E. Tinker, R. Coggins et al.
        Pages 93-112
      2. Mathilde E. Boon, L. P. Kok
        Pages 113-131
      3. Chris M. Bishop
        Pages 133-155
    3. Prediction and Control

      1. Yuan-Yih Hsu, Chien-Chun Yang
        Pages 157-189
      2. M. J. Willis, G. A. Montague, C. Peel
        Pages 191-219
      3. Arjen Jansen, Patrick van der Smagt, Frans Groen
        Pages 221-239
    4. Signal Processing

  4. Unsupervised Training

    1. Temporal Sequences — Reinforcement Learning

      1. Gerald Tesauro
        Pages 267-285
      2. Scott Miller, Ronald J. Williams
        Pages 287-303
    2. Mixed- Mode (Supervised and Unsupervised) Training

      1. Stephen Roberts, Lionel Tarassenko
        Pages 305-320
  5. Back Matter
    Pages 321-322

About this book


Applications of Neural Networks gives a detailed description of 13 practical applications of neural networks, selected because the tasks performed by the neural networks are real and significant. The contributions are from leading researchers in neural networks and, as a whole, provide a balanced coverage across a range of application areas and algorithms. The book is divided into three sections. Section A is an introduction to neural networks for nonspecialists. Section B looks at examples of applications using `Supervised Training'. Section C presents a number of examples of `Unsupervised Training'.
For neural network enthusiasts and interested, open-minded sceptics. The book leads the latter through the fundamentals into a convincing and varied series of neural success stories -- described carefully and honestly without over-claiming. Applications of Neural Networks is essential reading for all researchers and designers who are tasked with using neural networks in real life applications.


Phase Signal algorithms architecture calculus cognition design learning network networks neural networks pattern recognition reinforcement learning robot training

Editors and affiliations

  • Alan F. Murray
    • 1
  1. 1.The University of EdinburghUK

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