Neural Network Data Analysis Using SimulnetTM /
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, ge...
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| Format: | eBook |
| Language: | English |
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New York, NY :
Springer New York,
1998.
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| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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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| Item Description: | Electronic resource. |
| Physical Description: | 1 online resource (viii, 226 pages) |
| ISBN: | 9781461217466 (electronic bk.) 1461217466 (electronic bk.) |