Neural Network Data Analysis Using SimulnetTM
By: by Edward J. Rzempoluck
Publisher: Springer New York
Published: 1998
Place of publication: New York, NY
Language: eng
Format: [electronic resource] /
Number of pages: 226
ISBN-13: 9781461272625; 9781461217466
Subjects: Statistics; Computer science; Artificial intelligence
Description: This book and sofwtare package provide a complement to the traditional data analysis tools already widely available. It presents an introduction to the analysis of data using neural networks. Neural network functions discussed include multilayer feed-forward networks using error back propagation, genetic algorithm-neural network hybrids, generalized regression neural networks, learning quantizer networks, and self-organizing feature maps. In an easy-to-use, Windows-based environment it offers a wide range of data analytic tools which are not usually found together: these include genetic algorithms, probabilistic networks, as well as a number of related techniques that support these - notably, fractal dimension analysis, coherence analysis, and mutual information analysis. The text presents a number of worked examples and case studies using Simulnet, the software package which comes with the book. Readers are assumed to have a basic understanding of computers and elementary mathematics. With this background, a reader will find themselves quickly conducting sophisticated hands-on analyses of data sets.
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Also available in print.

Mode of access: World Wide Web.
Table of contents: title: Introduction; title: The Simulnet desktop. Data Analysis. Acquiring and conditioning network data; title: A Data Analysis Protocol; title: Glossary.