Dynamic learning rates algorithm for BPNN to forecast time series of dam security

被引：0

作者：

Hu, Daiping ^{[1
]}

Wu, Ruiming ^{[1
]}

Lei, Aizhong ^{[1
]}

Wang, Qi ^{[2
]}

机构：

[1] Antai College of Economics and Management, Shanghai Jiaotong University, Shanghai 200052, China

[2] Graduate School, Lanzhou University, Lanzhou 730000, China

来源：

Progress in Natural Science | 2007年 / 17卷 / 12期

关键词：

Dams - Learning algorithms - Network security - Neural networks - Time series;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Time series data of dam security have a large number of observed values and should be forecasted accurately in time. Neural networks have the powerful approach abilities of arbitrary functions and have been broadly utilized in many domains. In this paper, a dynamic learning rate training algorithm of back-propagation neural networks for time series forecasting is proposed and the networks with this algorithm are built to forecast time series of dam security. The application results demonstrate the effeciency of modelling and the effictiveness of forecasting.

引用

页码：1527 / 1530

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