Abstract
The Empirical Mode Decomposition (EMD) proposed by Huang et al. in 1998 shows remarkably effective in analyzing nonlinear signals. However, the boundary extension is one of the theoretical problems unsolved in EMD. In this paper, a novel boundary processing technique is proposed to deal with the border effect in EMD. An algorithm based on the sigma-pi neural network is used to extend signals before applying EMD. By virtue of this method, the frequency compression near the end is eliminated and errors caused by end effect are reduced. Verifications of the experimental signals show that the newly proposed boundary extension method is useful in practice.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Huang, N.E., Shen, Z., Long, S.R., et al.: The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis. Proc. R. Soc. Lond. A 1, 454–899 (1998)
Anna, L.: Variable Sampling of the Empirical Mode Decomposition of Two-Dimensional Signals. International Journal of Wavelets, Multiresolution and Information 4738, 1–8 (2002)
Zhao, J.: Improvement of the Mirror Extending in Empirical Mode Decomposition Method and the Technology for Eliminating Frequency Mixing. High Technology Letter 4, 40–47 (2002)
Kan, Z., He, M.: A Simple Boundary Process Technique for Empirical Mode Decomposition. IEEE Trans. PAMI 23, 234–241 (2004)
Shen, J., Shen, W., Sun, H.J., Yang, J.Y.: Fuzzy Neural Nets with Non-Symmetric Pi Membership Functions and Applications in Signal Processing and Image Analysis. Signal Processing 80, 965–983 (2000)
Datig, M., Schlurmann, T.: Performance and Limitations of the Hilbert–Huang transformation (HHT) with an Application to Iirregular Water Waves. Ocean Engineering 31, 1783–1834 (2004)
Nunes, J.C., Bouaoune, Y., Delechelle, E., Niang, O., Bunel, P.: Image Analysis by Bidimensional Empirical Mode Decomposition. Image and Vision Computing 21, 1019–1026 (2003)
Lenze, B.: Note on Interpolation on the Hypercube by Means of Sigma–pi Neural Networks. Neurocomputing 61, 471–478 (2004)
Courrieu, P.: Solving Ttime of Least Square Systems in Sigma-pi Unit Networks. Neural Information Processing 4, 39–47 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, Z. (2006). A Novel Boundary Extension Approach for Empirical Mode Decomposition. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_31
Download citation
DOI: https://doi.org/10.1007/11816157_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
eBook Packages: Computer ScienceComputer Science (R0)