Semi-parametric regression model prediction method based on Empirical Mode Decomposition
Semi-parametric regression model prediction method based on empirical mode decomposition was studied in this paper.Firstly, basic idea of the empirical mode decomposition was introduced, and the improved algorithm was proposed to mitigate the end effect in the iterative shift process. Secondly, least squares method was employed to estimate the parameter /3 based on the trend component of empirical mode decomposition, and the non-parametric g(·) was estimated through building the AR models of the intrinsic mode functions. The vector matrix was computed by Yule-Walker method. Finally, time series prediction of two nonlinear systems was analyzed based on the semi-parametric regression model. The results show that the proposed model predictive method is fit for nonlinear and nonstationary time series estimate.
Empirical Mode Decomposition end effect mirror extend least square polynomial
Zhang Qingjie Zhu Huayong Shen Lincheng
College of Mechatronics Engineering and Automation National University of Defense Technology Changsha, P.R.China
国际会议
The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)
沈阳
英文
330-335
2010-03-27(万方平台首次上网日期,不代表论文的发表时间)