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Garson, G. D. (2014). Neural Network Models. Asheboro, NC: Statistical Associates Publishers.

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About the author
Table of Contents
ISBN-10: 162638021X
ISBN-13: 978-1-62638-021-9
ASIN: B00HORW3HA (e-book counterpart to ISBN).
@c 2014 by G. David Garson and Statistical Associates Publishers. worldwide rights reserved in all languages and on all media. Permission is not granted to copy, distribute, or post e-books or passwords.



A graduate level introduction to and illustrated tutorial on neural network analysis.

Why we think it is important: Neural network analysis is a valuable tool for prediction of continuous target variables or classification of categorical target variables. It is robust for noisy and missing data, and is particularly useful when nonlinear relationships which cannot be addressed through data transformations or generalized link functions exist in the data.

New title in 2014:

The full content is now available from Statistical Associates Publishers. Click here.

Below is the unformatted table of contents.

Overview	6
Data examples	8
Artificial neural network software	9
Key concepts and terms	10
Abbreviations	10
Types of artificial neural network models	10
Multilayer perceptron  (MLP) models	10
Radial basis function (RBF) models	11
Kohonen self-organizing models	11
Networks, nodes, and weights	13
Models	16
Datasets	16
Training, recall, and learning	17
Training dataset considerations	18
Setting learning parameters	20
Convergence	22
Activation functions	23
Normalization	24
Multilayer perceptron (backpropagation) models	25
Overview	25
MLP models in SPSS	26
SPSS input for ANN-MLP	26
SPSS output for ANN-MLP	40
MLP models in SAS Enterprise Miner	49
Overview	49
Overview of SAS Enterprise Miner steps	50
MLP flow chart	60
Data Partition	60
Modeling	61
Architecture	62
Optimization	63
Model selection criterion	65
Output	66
Model Comparison	73
Scoring	75
Overview	77
SAS syntax	77
SAS output	78
Autoneural models in SAS	84
Overview	84
Example	85
Radial basis function models	86
Overview	86
RBF models, data order, and randomization	87
ANN-RBF models in SPSS	88
SPSS input for ANN-RBF	88
SPSS output for ANN-RBF	97
ANN-RBF models in SAS	109
Overview	109
Example using SAS Enterprise Miner	110
Neural network modeling in Stata	112
Assumptions	112
Data level	112
Adequate variance	112
Representative training cases	113
Randomization	113
Few outliers	113
Frequently asked questions	113
What are the “NIST Studies” in relation to ANN?	113
What is a backpropagation model?	114
How can I tell if my results are significant?	116
How can I improve the generalization of my model?	117
Explain neural weights	118
Explain activation (transfer) functions	119
Explain settings for learning rate parameters	121
What are strategies for model complexity vs. model parsimony?	123
Explain quartile analysis	124
Is generalized ANN available?	125
Do I need to transform my input variables?	125
Do I need to standardize my input variables?	125
How should I code binary variables?	127
How do I handle “DK= Don’t Know” and similar codes for my dependent variable?	127
What are pretrained networks?	128
What is a PNN model?	128
What is a GRNN model?	128
What are “constructive algorithms” in ANN-RBF?	129
What software is available to implement ANN models?	129
What are some drawbacks to use of ANN?	129
Bibliography	132
Appendix A: SAS Optimized Data Step Code	136
Appendix B: SAS Results for the “Score” node	141
Pagecount: 144