| Description |
xi, 174 pages : illustrations ; 24 cm |
| Contents |
Machine derived contents note: Table of contents: Preface.- Key concepts in neural networks.- Backpropagation.- Neurons in the Brain.- The Fundamental System of Differential Equations.- Synchronous and Discrete Networks.- Linear Capacity.- Capacity from a Signal to Noise Ratio.- Neural Networks and Markov Chains.- Bibliography.- Index |
| Summary |
Neural Network Models: Theory and Projects concentrates on the essential parameters and results that will enable the reader to design hardware or software implementations of neural networks and to assess critically existing commercial products. It is suitable for final year, postgraduate and doctoral students in engineering, computing, applied mathematics, physics and biomedical systems, and will also be of interest to those working in science and industry who wish to obtain a firm grounding in the subject |
|
Providing an in-depth treatment of neural network models, this volume explains and proves the main results in a clear and accessible way. Topics covered include key concepts in neural networks, backpropagation, neurons in models of the brain, synchronous and discrete networks, differential mathematics, linear capacity, capacity from a signal to noise ratio, and neural networks and Markov chains. Each chapter ends with a suggested project designed to help the reader develop an integrated knowledge of the theory, placing it within a practical application domain |
| Bibliography |
Includes bibliographical references (pages 159-168) and index |
| Subject |
Neural networks (Computer science)
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| LC no. |
97014010 |
| ISBN |
3540761292 (paperback) |
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