Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN....Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.展开更多
Wavelets are applied to detect the jumps in a heteroscedastic regression model. By the empirical wavelet coefficients of the conditional mean and the conditional variance of the time series under consideration, it is ...Wavelets are applied to detect the jumps in a heteroscedastic regression model. By the empirical wavelet coefficients of the conditional mean and the conditional variance of the time series under consideration, it is shown that the wavelet coefficients exhibit high peaks near the jump points, based on which a procedure is developed to identify and then to locate the jumps. All estimators are proved to be consistent.展开更多
基金The National Natural Science Foundation of China(No.50479017).
文摘Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.
基金the National Natural Science Foundation of China (No.19571010).
文摘Wavelets are applied to detect the jumps in a heteroscedastic regression model. By the empirical wavelet coefficients of the conditional mean and the conditional variance of the time series under consideration, it is shown that the wavelet coefficients exhibit high peaks near the jump points, based on which a procedure is developed to identify and then to locate the jumps. All estimators are proved to be consistent.
