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

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

Research output: Contribution to journalArticlepeer-review

1241 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)135-156
Number of pages22
JournalAdvances in Adaptive Data Analysis
Volume2
Issue number2
DOIs
StatePublished - Apr 2010

Keywords

  • complementary ensemble empirical mode decomposition (CEEMD)
  • Ensemble empirical mode decomposition (EEMD)
  • intermittence
  • noise enhanced method

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