Neural Network Models: An Analysis (Lecture Notes in Control & Information Sciences)

De Wilde, Philippe

ISBN 10: 3540199950 / ISBN 13: 9783540199953

Published by Springer Verlag, 1995

About this Item

A copy that has been read, remains in good condition. All pages are intact, and the cover is intact. The spine and cover show signs of wear. Pages can include notes and highlighting and show signs of wear, and the copy can include "From the library of" labels or previous owner inscriptions. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships via media mail.

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Bibliographic Details

• Title: Neural Network Models: An Analysis (Lecture Notes in Control & Information Sciences)
• Publisher: Springer Verlag
• Publication Date: 1995
• Language: English
• ISBN 10: 3540199950
• ISBN 13: 9783540199953
• Binding: Soft cover
• Condition: good

About this title

Synopsis

Providing an in-depth treatment of the main topics in neural networks this volume concentrates on multilayer networks and completely connected networks, as well as discussing both analog and digital networks. The central themes are dynamical behaviour, attractors and capacity. Boltzmann machines are also discussed. The subject is developed from scratch and does not use statistical physics. Because the volume adopts a pedagogical approach, explaining all steps in full, the style which the book takes it will appeal to engineers, computer scientists and applied mathematicians. The reader will learn the parameters and results that are most important for the design of neural networks and will be able to critically assess the existing commercial products or design a hardware or software implementation for him- or herself.

From the Back Cover

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. 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.