Artificial neural networks : an introduction to ANN theory and practice
Author(s)
Bibliographic Information
Artificial neural networks : an introduction to ANN theory and practice
（Lecture notes in computer science, 931）
Springer, c1995
- : gw
- : us
Available at 54 libraries
Note
Includes bibliographical references, and supporting general literature (p. [289]-293)
Description and Table of Contents
Description
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.
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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