Applications of Neural Networks

1. Front Matter

   Pages i-xii

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2. Introduction

   1. Neural Architectures and Algorithms

      • Alan Murray

      Pages 1-33

3. Supervised Training

   1. Pattern Recognition and Classification

      1. Face Finding in Images

         • John M. Vincent

         Pages 35-70

      2. Sex Recognition from Faces Using Neural Networks

         • B. Golomb, T. Sejnowski

         Pages 71-92

   2. Diagnosis and Monitoring

      1. ANN Based Classification of Arrhythmias

         • M. Jabri, S. Pickard, P. Leong, Z. Chi, E. Tinker, R. Coggins et al.

         Pages 93-112

      2. Classification of Cells in Cervical Smears

         • Mathilde E. Boon, L. P. Kok

         Pages 113-131

      3. Multiphase flow monitoring in oil pipelines

         • Chris M. Bishop

         Pages 133-155

   3. Prediction and Control

      1. Electrical Load Forecasting

         • Yuan-Yih Hsu, Chien-Chun Yang

         Pages 157-189

      2. On the Application of Artificial Neural Networks to Process Control

         • M. J. Willis, G. A. Montague, C. Peel

         Pages 191-219

      3. Nested Networks for Robot Control

         • Arjen Jansen, Patrick van der Smagt, Frans Groen

         Pages 221-239

   4. Signal Processing

      1. Adaptive Equalisation using Neural Networks

         • Sheng Chen

         Pages 241-265

4. Unsupervised Training

   1. Temporal Sequences — Reinforcement Learning

      1. TD-Gammon: A Self-Teaching Backgammon Program

         • Gerald Tesauro

         Pages 267-285

      2. Temporal Difference Learning: A Chemical Process Control Application

         • Scott Miller, Ronald J. Williams

         Pages 287-303

   2. Mixed- Mode (Supervised and Unsupervised) Training

      1. Automated Sleep EEg Analysis using an RBF Network

         • Stephen Roberts, Lionel Tarassenko

         Pages 305-320

5. Back Matter

   Pages 321-322

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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

• The University of Edinburgh, UK

  Alan F. Murray

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