Study on Methods for Improving LMD End Effect

Article Preview

Abstract:

The LMD is a new method for analyzing non-stationary signals. It can decompose complicated signals into a set of single-component signals, each of which has physical sense. But peforming the LMD will produce end effects which make results distortion. After analyzing the reasons for these, the ariticle takes adcantage of three normal methods to overcome the end effects of LMD. Experimental results of three models showed that the method of self-adaptive waveform matching was better than other methods.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

559-566

Citation:

Online since:

August 2016

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2016 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] J. S. Smith, The Local Mean Decomposition and Its Application to EEG Perception Data, J. Royal Soc. Interf. 2(5) (2005) 443 454.

DOI: 10.1098/rsif.2005.0058

Google Scholar

[2] N. E. Huang, Z. Shen, S. R. Long, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. Lond. Seris A, (1998) 903-995.

DOI: 10.1098/rspa.1998.0193

Google Scholar

[3] N. E. Huang, Z. Shen, S. R. Long, et al. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis, Proc. R. Soc. Lond. A, 454(1971) (1998) 903-995.

DOI: 10.1098/rspa.1998.0193

Google Scholar

[4] N. E. Huang, Z. Shen, S. R. Long, A New View of Nonlinear Water Waves: the Hilbert Spectrum, Annu. Rev. Fluid Mech. 31(1) (1999) 417-457.

DOI: 10.1146/annurev.fluid.31.1.417

Google Scholar

[5] D. M. Klionski, N. I. Oreshko, V. V. Geppener, et al. Applications of empirical mode decomposition for processing nonstationary signals, Pattern Recog. Image Anal. 18(3) (2008) 390-399.

DOI: 10.1134/s105466180803005x

Google Scholar

[6] D. P. Madnic, M. Golz, A. Kuh, et al. Signal processing techniques for knowledge ertraction and information fusion, Berlin: Springer, (2007).

Google Scholar

[7] Y. S. Lee, S. Tsakirtzis, A. F. Vakakis, et al. Physics-based foundation for empirical mode decomposition, AIAA J. 47(12) (2009) 2938-2963.

DOI: 10.2514/1.43207

Google Scholar

[8] D. J. Huang, J. P. Zhao, J. L. Su, Praetical implementation of the Hilbert-Hunag Trnasofm algorithm, Acta. Oceanologica. Sinica. 25(1) (2003) 1-11.

Google Scholar

[9] Q. Gai, X. J. Ma, H. Y. Zhang, et al. New method for processing end effect in local wavemethod, J. Dalian U. Tech. 42(1) (2002) 115-117.

Google Scholar

[10] Y. J. Deng, W. Wang, C. C. Qian, et al. Comments and modifieations on EMD method, Chinese Sci. Bull. 46(3) (2001) 257-263.

Google Scholar

[11] Y. J. Deng, W. Wang, C. C. Qian, et al. Comments and modifications on EMD method, Chinese Sci. Bull. 46(3) (2001) 257-263.

Google Scholar

[12] K. Zhang, J. S. Cheng, Y. Yang, Processing Method for End Effects of Local Mean Decomposition Based on Self-adaptive Waveform Matching Extending, China Mach. Eng. (4) (2010) 457-462.

Google Scholar

[13] L. Lin, T. Zhou, L. Yu, Edge Effect Processing Technique in EMD Algorithm, Comp. Eng. 35(23) (2009) 265-268.

Google Scholar