TY - JOUR
T1 - Complementary ensemble empirical mode decomposition
T2 - A novel noise enhanced data analysis method
AU - Yeh, Jia Rong
AU - Shieh, Jiann Shing
AU - Huang, Norden E.
PY - 2010/4
Y1 - 2010/4
N2 - 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.
AB - 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.
KW - complementary ensemble empirical mode decomposition (CEEMD)
KW - Ensemble empirical mode decomposition (EEMD)
KW - intermittence
KW - noise enhanced method
UR - http://www.scopus.com/inward/record.url?scp=79956369785&partnerID=8YFLogxK
U2 - 10.1142/S1793536910000422
DO - 10.1142/S1793536910000422
M3 - 期刊論文
AN - SCOPUS:79956369785
SN - 1793-5369
VL - 2
SP - 135
EP - 156
JO - Advances in Adaptive Data Analysis
JF - Advances in Adaptive Data Analysis
IS - 2
ER -