PSO-SVM method based on elimination of end effects in EMD

End effects of EMD (empirical mode decomposition) make a serious distortion of the decomposition result. In order to reduce the end effects in the process of decomposition, support vector machine (SVM) which is a kind of intelligent algorithm is combined with EMD, then a solution to the end effects problem during the course of decomposition using SVM model is proposed. Firstly, one or more extreme values are obtained by extending two endpoints of the original data with SVM. Moreover, in order to get more reasonable extension at endpoint, SVM algorithm is combined with particle swarm algorithm (PSO) to optimize the parameters, and the extension of two endpoints will be more accurate, then the end-points of cubic spline curve will not have large swing so as to achieve that intrinsic mode functions (IMF) of EMD are more accurate and reliable. Simulation results indicate that the extension method for data based on PSO-SVM method can restrain the end effects effectively.