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Abstract: The treatment of end effects is one of the most important open problems related to the EMD (Empirical Mode Decomposition) method. This work proposes a new approach that couples the mirror expansion with the extrapolation prediction of regression function to solve this problem. The algorithm includes two steps: the extrapolation of the signal through Support Vector (SV) regression at both endpoints to form the primary expansion signal, then the primary signal is further expanded through extrema mirror expansion and EMD is performed on the resulting signal to obtain reduced end effects. If there is not enough length for the signal to meet the need of finding the length of the data available for expanding the signal, a direct extrapolation towards the outside of the signal at the endpoint is executed by the estimate model, and the length of extrapolation points is controlled by the first local extremum. Applications of the proposed approach to the decomposition of a digital modeling signal and three segment signals from the observed earthquake signal by the EMD method are presented, and all of the results are compared with those on the basis of the traditional mirror expansion approach and the extrapolation estimate expansion based on the SV regression, which shows that the most satisfactory result can be obtained for the elimination of end effects in EMD method by mirror image coupled with SV regression. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]