Neural Network Data Analysis Using SimulnetTM

This book PDF is perfect for those who love Computers genre, written by Edward J. Rzempoluck and published by Springer Science & Business Media which was released on 06 December 2012 with total hardcover pages 233. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Neural Network Data Analysis Using SimulnetTM books below.

Neural Network Data Analysis Using SimulnetTM
Author : Edward J. Rzempoluck
File Size : 51,7 Mb
Publisher : Springer Science & Business Media
Language : English
Release Date : 06 December 2012
ISBN : 9781461217466
Pages : 233 pages
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Neural Network Data Analysis Using SimulnetTM by Edward J. Rzempoluck Book PDF Summary

This book and software package complements the traditional data analysis tools already widely available. It presents an introduction to the analysis of data using neural network functions such as multilayer feed-forward networks using error back propagation, genetic algorithm-neural network hybrids, generalised regression neural networks, learning quantizer networks, and self-organising 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: genetic algorithms, probabilistic networks, as well as a number of related techniques that support these. Readers are assumed to have a basic understanding of computers and elementary mathematics, allowing them to quickly conduct sophisticated hands-on analyses of data sets.

Neural Network Data Analysis Using SimulnetTM

This book and software package complements the traditional data analysis tools already widely available. It presents an introduction to the analysis of data using neural network functions such as multilayer feed-forward networks using error back propagation, genetic algorithm-neural network hybrids, generalised regression neural networks, learning quantizer networks, and self-organising feature

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Neural Network Data Analysis Using Simulnet

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