Guide to Neural Computing Applications

By

  • Lionel Tarassenko, Professor of Electrical and Electronic Engineering, Oxford University, UK

Neural networks have shown enormous potential for commercial exploitation over the last few years but it is easy to overestimate their capabilities. A few simple algorithms will learn relationships between cause and effect or organise large volumes of data into orderly and informative patterns but they cannot solve every problem and consequently their application must be chosen carefully and appropriately.This book outlines how best to make use of neural networks. It enables newcomers to the technology to construct robust and meaningful non-linear models and classifiers and benefits the more experienced practitioner who, through over familiarity, might otherwise be inclined to jump to unwarranted conclusions. The book is an invaluable resource not only for those in industry who are interested in neural computing solutions, but also for final year undergraduates or graduate students who are working on neural computing projects. It provides advice which will help make the best use of the growing number of commercial and public domain neural network software products, freeing the specialist from dependence upon external consultants.
View full description

Audience

Professional engineers, industrial managers, postgraduates and researchers in computer science/electronic engineering.

 

Book information

  • Published: January 1998
  • Imprint: BUTTERWORTH HEINEMANN
  • ISBN: 978-0-340-70589-6

Reviews

An excellent tutorial and practical users guide - far more accessible than the competition.,Professor Alan F. Murray, University of Edinburgh, UK. ... deserves a place on the neural network practitioners bookshelf ...,Proceedings of the Institution of Electrical Engineers,



Table of Contents

Mathematical background for neural computing * Managing a neural computing project * Identifying applications and assessing their feasibility * Neural computing hardware and software * Collecting and preparing data * Designing, training and testing of the prototype * Case studies * Error propagation algorithm for weight updated in a MLP * Use of Bayes theorem to compensate for different prior probabilities.