Artificial Neural Networks: An Introduction to ANN Theory and Practice
Author:   P.J. Braspenning ,  F. Thuijsman ,  A.J.M.M. Weijters
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Edition:   1995 ed.
Volume:   931
ISBN:  

9783540594888


Pages:   299
Publication Date:   02 June 1995
Format:   Paperback
Availability:   Out of print, replaced by POD   Availability explained
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Artificial Neural Networks: An Introduction to ANN Theory and Practice


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Overview

This book presents carefully revised versions of tutorial lectures given during a School on Artificial Neural Networks for the industrial world held at the University of Limburg in Maastricht, Belgium. The major ANN architectures are discussed to show their powerful possibilities for empirical data analysis, particularly in situations where other methods seem to fail. Theoretical insight is offered by examining the underlying mathematical principles in a detailed, yet clear and illuminating way. Practical experience is provided by discussing several real-world applications in such areas as control, optimization, pattern recognition, software engineering, robotics, operations research, and CAM.

Full Product Details

Author:   P.J. Braspenning ,  F. Thuijsman ,  A.J.M.M. Weijters
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   1995 ed.
Volume:   931
Dimensions:   Width: 15.50cm , Height: 1.60cm , Length: 23.30cm
Weight:   0.970kg
ISBN:  

9783540594888


ISBN 10:   3540594884
Pages:   299
Publication Date:   02 June 1995
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
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

Introduction: Neural networks as associative devices.- Backpropagation networks for Grapheme-Phoneme conversion: A non-technical introduction.- Back Propagation.- Perceptrons.- Kohonen network.- Adaptive Resonance Theory.- Boltzmann Machines.- Representation issues in Boltzmann machines.- Optimisation networks.- Local search in combinatorial optimization.- Process identification and control.- Learning controllers using neural networks.- Key issues for successful industrial neural-network applications: An application in geology.- Neural cognodynamics.- Choosing and using a neural net.

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