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

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.