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Applications of Neural Networks / Edition 1
     

Applications of Neural Networks / Edition 1

by Alan Murray
 

ISBN-10: 0792394429

ISBN-13: 9780792394426

Pub. Date: 12/31/1994

Publisher: Springer US

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

Overview

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.

Product Details

ISBN-13:
9780792394426
Publisher:
Springer US
Publication date:
12/31/1994
Edition description:
1995
Pages:
322
Product dimensions:
6.14(w) x 9.21(h) x 0.04(d)

Related Subjects

Table of Contents

Preface. Section A: Introduction. 1. Neural Architectures and Algorithms; A. Murray. Section B: Supervised Training. B.1: Pattern Recognition and Classification. 2. Face Finding in Images; J.M. Vincent. 3. Sex Recognition from Faces Using Neural Networks; B. Golomb, T. Sejnowski. B.2: Diagnosis and Monitoring. 4. ANN Based Classification of Arrhythmias; M. Jabri, S. Pickard, P. Leong, Z. Chi, E. Tinker, R. Coggins, B. Flower. 5. Classification of Cells in Cervical Smears; M. Boom, L.P. Kok. 6. Multiphase Flow Monitoring in Oil Pipelines; C.M. Bishop. B.3: Prediction and Control. 7. Electrical Load Forecasting; Yuan-Yih Hsu, Chien-Chun Yang. 8. On the Application of Artificial Neural Networks to Process Control; M.J. Willis, G.A. Montague, C. Peel. 9. Nested Networks for Robot Control; A. Jansen, P. van der Smagt, F. Groen. B.4: Signal Processing. 10. Adaptive Equalisation Using Neural Networks; Sheng Chen. Section C: Unsupervised Training. C.1: Temporal Sequences — Reinforcement Learning. 11. TD-Gammon: a Self Teaching Backgammon Program; G. Tesauro. 12. Temporal Difference Learning: a Chemical Process Control Application; S. Miller, R.J. Williams. C.2: Mixed-Mode (Supervised and Unsupervised) Training. 13. Automated Sleep EEG Analysis Using an RBF Network; S. Roberts, L. Tarassenko.

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