Neural Network Models: Theory and Projects
Author:   Philippe de Wilde
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Edition:   2nd ed. 1997
ISBN:  

9783540761297


Pages:   174
Publication Date:   30 May 1997
Format:   Paperback
Availability:   Out of print, replaced by POD   Availability explained
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Neural Network Models: Theory and Projects


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Overview

Providing an in-depth treatment of neural network models, this volume explains and proves the main results in a clear and accessible way. It presents the essential principles of nonlinear dynamics as derived from neurobiology, and investigates the stability, convergence behaviour and capacity of networks. Also included are sections on stochastic networks and simulated annealing, presented using Markov processes rather than statistical physics, and a chapter on backpropagation. 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. 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.

Full Product Details

Author:   Philippe de Wilde
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   2nd ed. 1997
Dimensions:   Width: 15.50cm , Height: 1.00cm , Length: 23.50cm
Weight:   0.600kg
ISBN:  

9783540761297


ISBN 10:   3540761292
Pages:   174
Publication Date:   30 May 1997
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of print, replaced by POD   Availability explained
We will order this item for you from a manufatured on demand supplier.

Table of Contents

1 Key concepts in neural networks.- 2 Backpropagation.- 3 Neurons in the Brain.- 4 The Fundamental System of Differential Equations.- 5 Synchronous and Discrete Networks.- 6 Linear Capacity.- 7 Capacity from a Signal to Noise Ratio.- 8 Neural Networks and Markov Chains.

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