Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone

被引：84
|
作者：
Bandyopadhyay, G.
Chattopadhyay, S.
机构：
[1] Department of Information Technology, Pailan College of Management and Technology, West Bengal University of Technology
[2] Saint Xavier's College, University of Calcutta
来源：
INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY | 2007年 / 4卷 / 01期
关键词：
arosa; total ozone; single-hidden-layer; artificial neural network; multiple linear regression; forecast;
D O I：
10.1007/BF03325972
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
Present paper endeavors to develop predictive artificial neural network model for forecasting the mean monthly total ozone, concentration over Arosa, Switzerland. Single hidden layer neural network models with variable number of nodes have been developed and their performances have been evaluated using the method of least squares and error estimation. Their performances have been compared with multiple linear regression model. Ultimately, single-hidden-layer model with 8 hidden nodes have been identified as the best predictive model.
引用
收藏
页码：141 / 149
页数：9
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