Paper Info
Title
Complementary Ensemble Empirical Mode Decomposition: A Novel Noise Enhanced Data Analysis Method
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.
Year
DOI
Venue
2010
10.1142/S1793536910000422
ADVANCES IN DATA SCIENCE AND ADAPTIVE ANALYSIS
Keywords
Field
DocType
Ensemble empirical mode decomposition (EEMD), intermittence, noise enhanced method, complementary ensemble empirical mode decomposition (CEEMD)
Magnitude (mathematics),Statistics,Mathematics,Signal reconstruction,Hilbert–Huang transform
Journal
Volume
Issue
ISSN
2
2
2424-922X
Citations 
PageRank 
References 
63
4.07
2
Authors
3
Name
Order
Citations
PageRank
Jia-Rong Yeh1685.99
Jiann Shing Shieh222428.44
Norden E. Huang395081.56