An improved training algorithm of neural networks for time series forecasting

被引：0

作者：

Hu, Daiping ^{[1
]}

Wu, Ruiming ^{[1
]}

Chen, Dezhi ^{[1
]}

Don, Huiming ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Antai Coll Econ & Management, Shanghai 200052, Peoples R China

来源：

MICAI 2007: ADVANCES IN ARTIFICIAL INTELLIGENCE | 2007年 / 4827卷

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Neural network approaches for time series forecasting, which have the property of simpleness, nonlinearity and effectiveness, have been broadly utilized in many domains. In this paper, an improved training algorithm of back-propagation neural network for time series forecasting by using dynamic learning rate in the training process is proposed. The results of some studied cases demonstrate this algorithm can increase the efficiency of neural network training and the precision of forecasts.

引用

页码：550 / +

页数：2

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