Overview

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.

Full Product Details

Author:   Edward J. Rzempoluck
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Dimensions:   Width: 17.00cm , Height: 2.20cm , Length: 24.40cm
Weight:   0.602kg
ISBN:  

9780387982557

ISBN 10:   0387982558
Pages:   226
Publication Date:   12 December 1997
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

Scope of this Text.- What Is Expected from the Reader.- An Outline.- Computer Requirements.- 1 The Simulnet Desktop.- Desktop Components.- 2 Data Analysis.- The Substantive Question.- Neural Network Analysis.- Genetic Algorithms and Neural Networks.- The Probabilistic Network.- The Vector Quantizer Network.- Assessing the Significance of Network Results.- Network Application Examples.- Fractal Dimension Analysis.- Fourier Analysis.- Eigenvalue Analysis.- Coherence and Phase Analysis.- Mutual Information Analysis.- Correlation and Covariance Analysis.- 3 Acquiring and Conditioning Network Data.- Data Specification.- Data Collection.- Data Inspection.- Data Conditioning.- Detrend-Order 0.- Standardize Columns.- Frequency Filtering.- Principal Component Analysis.- Principal Component Data Reduction.- 4 A Data Analysis Protocol.- A Preprocessing Checklist.- Analyzing Experimental Data.

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