Applications of empirical mode decomposition for processing nonstationary signals

被引：35

作者：

Klionski D.M. ^{[1
]}

Oreshko N.I. ^{[1
]}

Geppener V.V. ^{[1
]}

Vasiljev A.V. ^{[1
]}

机构：

[1] Research and Engineering Center, Saint Petersburg Electrotechnical University, St. Petersburg

来源：

Pattern Recognition and Image Analysis | 2008年 / 18卷 / 3期

关键词：

Empirical Mode Decomposition; Instantaneous Frequency; Initial Signal; Intrinsic Mode Function; Lower Envelope;

D O I：

10.1134/S105466180803005X

中图分类号：

学科分类号：

摘要：

Empirical mode decomposition (EMD) has recently been pioneered by Huang as a fully data-driven technique aimed at decomposing nonstationary signals in a set of "Intrinsic mode functions" (IMFs, Empirical modes). We will report on the main theoretical aspects of EMD, its extensive possibilities, and various contemporary applications. We will pay attention to detrending; denoising; Hilbert-Huang time-frequency analysis; and a very perspective and actual scientific direction known as Data Mining, which involves such problems as segmentation, cluster-analysis (clustering), classification, etc. © 2008 Pleiades Publishing, Ltd.

引用

页码：390 / 399

页数：9

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