A Research on Local Mean in Empirical Mode Decomposition

Authors:
Yong-Ping Huang;Xue-Yao Li;Ru-Bo Zhang
Affiliations:
National Laboratory on Machine Perception, Peking University, Beijing, P.R. China, College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, P.R. China;National Laboratory on Machine Perception, Peking University, Beijing, P.R. China, College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, P.R. China;National Laboratory on Machine Perception, Peking University, Beijing, P.R. China, College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, P.R. China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Year:
2007

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Abstract

The conventional Empirical mode decomposition (EMD) method uses envelope mean interpolated by cubic spline fitting, which is sensitive to extrema. An efficient method for finding the local mean is generated by using support vector regression machines. The analysis results indicate that the proposed algorithm has higher performance in the ability of frequency separation, insensitive to the sampling frequency and can eliminate mode mixing in small-amplitude sine waves intermittence.