期刊
ADVANCES IN DATA SCIENCE AND ADAPTIVE ANALYSIS
卷 2, 期 2, 页码 135-156出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S1793536910000422
关键词
Ensemble empirical mode decomposition (EEMD); intermittence; noise enhanced method; complementary ensemble empirical mode decomposition (CEEMD)
资金
- National Science Council (NSC) of Taiwan [NSC96-2221-E-155-015-MY3-2, NSC 98-2627-B-008-004, NSC 98-2611-M-008-004]
- Federal Highway
The phenomenon of mode-mixing caused by intermittence signals is an annoying problem in Empirical Mode Decomposition (EMD) method. The noise assisted method of Ensemble EMD (EEMD) has not only effectively resolved this problem but also generated a new one, which tolerates the residue noise in the signal reconstruction. Of course, the relative magnitude of the residue noise could be reduced with large enough ensemble, it would be too time consuming to implement. An improved algorithm of noise enhanced data analysis method is suggested in this paper. In this approach, the residue of added white noises can be extracted from the mixtures of data and white noises via pairs of complementary ensemble IMFs with positive and negative added white noises. Though this new approach yields IMF with the similar RMS noise as EEMD, it effectively eliminated residue noise in the IMFs. Numerical experiments were conducted to demonstrate the new approach and also illustrate the problems of mode splitting and translation.
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