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

作　　者：Hu Daiping Wu Ruiming Lei Aizhong Wang Qi 

机构地区：[1]Antai College of Economics ＆ Management, Shanghai Jiaotong University, Shanghai 200052, China [2]Graduate School, Lanzhou University, Lanzhou 730000, China

出　　处：《Progress in Natural Science:Materials International》2007年第12期1527-1530,共4页自然科学进展·国际材料（英文版）

基　　金：Supported by National Natural Science Foundation of China (Grant No .70671067);Youth Research Fund of Antai College of Economics & Management

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 ablilities 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 demonostrate the efficiency of modelling and the effictiveness of forecasting.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 ablilities 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 demonostrate the efficiency of modelling and the effictiveness of forecasting.

关 键 词：neural network dynamic learning rate forecasting dam security. 

分 类 号：TV5[水利工程—水利水电工程]