Guide to Neural Computing Applications

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Elsevier, Jan 30, 1998 - Computers - 160 pages
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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.


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Chapter 2 Mathematical background for neural computing
Chapter 3 Managing a neural computing project
Chapter 4 Identifying applications and assessing their feasibility
Chapter 5 Neural computing hardware and software
Chapter 6 Collecting and preparing data
Chapter 7 Design training and testing of the prototype
Chapter 8 The case studies
Chapter 9 More advanced topics
The error backpropagation algorithm for weight updates in an MLP
Use of Bayes theorem to compensate for different prior probabilities

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Page 2 - Whatever the name, all these models attempt to achieve good performance via dense interconnection of simple computational elements. In this respect, artificial neural net structure is based on our present understanding of biological nervous systems. Neural net models have greatest potential in areas such as speech and image recognition where many hypotheses are pursued in parallel, high computation rates are required, and the current best systems are far from equaling human performance.

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