Item description for Neural Network Models by Philippe de Wilde...
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 onthe 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.
Promise Angels is dedicated to bringing you great books at great prices. Whether you read for entertainment, to learn, or for literacy - you will find what you want at promiseangels.com!
Est. Packaging Dimensions: Length: 9.28" Width: 6.17" Height: 0.49" Weight: 0.66 lbs.
Release Date Jul 11, 1997
ISBN 3540761292 ISBN13 9783540761297
Availability 144 units. Availability accurate as of May 23, 2017 06:38.
Usually ships within one to two business days from La Vergne, TN.
Orders shipping to an address other than a confirmed Credit Card / Paypal Billing address may incur and additional processing delay.
Reviews - What do customers think about Neural Network Models?
A clear and concise introduction to neural network dynamics Dec 19, 2000
This book is an excellent introduction to neural networks for the reader with a good mathematics background. The first few chapters describe popular and useful algorithms for implementing neural networks (Hopfield, backpropagation). Much of the book is devoted to the theory behind these networks (capacity, convergence and the dynamical systems approach, Markov chains). [This gave me, a total novice before reading the book, a much better idea of how these networks function, so much so that I can implement them in software.] Complementing the theory are numerous stimulating projects that illustrate many applications and open questions in the field.
De Wilde's book is very clearly written. Its compact size is a joy in this age of mammoth, but often poorly-written and substanceless, textbooks.