Neural Network Data Analysis Using Simulnet (TM) (Hardcover)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Release date:	December 1997
First published:	November 1997
Authors:	Edward J. Rzempoluck
Dimensions:	244 x 170 x 22mm (L x W x T)
Format:	Hardcover
Pages:	226
ISBN-13:	978-0-387-98255-7
Categories:	Books > Computing & IT > Applications of computing > Databases > Data capture & analysis Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
LSN:	0-387-98255-8
Barcode:	9780387982557

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