DocumentCode :
175417
Title :
Research on restraining the end effect of EMD based on grey prediction model
Author :
Yong-tao Zong ; Yan-xia Shen ; Zhi-cheng Ji ; Ding-hui Wu ; Ting-long Pan
Author_Institution :
Inst. of Electr. Autom., Jiangnan Univ., Wuxi, China
fYear :
2014
fDate :
May 31 2014-June 2 2014
Firstpage :
214
Lastpage :
217
Abstract :
Empirical Mode Decomposition(EMD) is an advanced method for analyzing non-stationary signal, but there is an involved end issue in the course of getting two envelops of the data using spline interpolation. In this paper a novel method based on grey prediction model is proposed to restrain the end effect of empirical mode decomposition. In the grey prediction endpoint extension process, based on the theory of GM(1,1) prediction model, the endpoints are extended respectively to approach the variation tendency of the original data sequences. Simulation result shows that the proposed method can restrain end effect effectively and the parameters of grey prediction are easy to determine.
Keywords :
grey systems; interpolation; queueing theory; splines (mathematics); EMD; GM(1,1) prediction model; empirical mode decomposition; end effect; grey prediction endpoint extension process; grey prediction model; nonstationary signal analysis; original data sequences; spline interpolation; variation tendency; Biological system modeling; Data models; Empirical mode decomposition; Interpolation; Mirrors; Predictive models; Splines (mathematics); Empirical mode decomposition; End effect; Fault diagnosis; Grey prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
Type :
conf
DOI :
10.1109/CCDC.2014.6852147
Filename :
6852147
Link To Document :
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