Investigation of the Empirical Mode Decomposition Based on Genetic Algorithm Optimization Schemes

Empirical mode decomposition (EMD) has lately received much attention due to the many interesting features that exhibits. However it lacks a strong theoretical basis which would allow a performance analysis and hence the enhancement and optimization of the method in a systematic way. In this paper, an investigation of EMD is attempted in an alternative way. The interpolation points and the piecewise interpolating polynomials for the formation of the upper and lower envelopes of the signal are optimized based on a genetic algorithm framework revealing important characteristics of the method which where previously hidden. As a result, novel directions for both the performance enhancement and the theoretical investigation of the method are unveiling.

[1]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[2]  James F. Kaiser,et al.  The use of a masking signal to improve empirical mode decomposition , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[3]  Gabriel Rilling,et al.  on the Influence of Sampling on the Empirical Mode Decomposition , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[4]  Hualou Liang,et al.  Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease , 2005, IEEE Transactions on Biomedical Engineering.

[5]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[6]  C. Pozrikidis.  Numerical computation in science and engineering , 1998 .

[7]  Xiaodong Wang,et al.  Analysis of partial discharge signal using the Hilbert-Huang transform , 2006, IEEE Transactions on Power Delivery.