Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method

Jia Rong Yeh, Jiann Shing Shieh, Norden E. Huang

研究成果: 雜誌貢獻期刊論文同行評審

1222 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
頁(從 - 到)135-156
頁數22
期刊Advances in Adaptive Data Analysis
2
發行號2
DOIs
出版狀態已出版 - 4月 2010

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