Neural Networks in Applied Statistics
作者:
HalS. Stern,
期刊:
Technometrics
(Taylor Available online 1996)
卷期:
Volume 38,
issue 3
页码: 205-214
ISSN:0040-1706
年代: 1996
DOI:10.1080/00401706.1996.10484497
出版商: Taylor & Francis Group
关键词: Nonlinear regression;Time series prediction
数据来源: Taylor
摘要:
Artificial neural networks are computer algorithms or computer programs derived in part from attempts to model the activity of nerve cells. They have been applied to pattern recognition, classification, and optimization problems in the physical and chemical sciences, as well as in other fields. We introduce the principles of the multilayer feedforward network that is among the most commonly used neural networks in practical problems. The relevance of neural network models for the applied statistician is considered using a time series prediction problem as an example. The multilayer feedforward neural network uses a nonlinear function of the predictors to obtain predictions for future time series values. We illustrate the considerations involved in specifying a neural network model and evaluate the accuracy of neural network models relative to the accuracy obtained using other computer-intensive, nonmodel-based techniques.
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