A machine learning enhanced empirical mode decomposition

被引：11

作者：

Looney, D. ^{[1
]}

Mandic, D. P. ^{[1
]}

机构：

[1] Univ London Imperial Coll Sci Technol & Med, London SW7 2AZ, England

来源：

2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12 | 2008年

关键词：

empirical mode decomposition (EMD); machine learning; feature fusion; adaptive filtering;

D O I：

10.1109/ICASSP.2008.4518005

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Empirical mode decomposition (EMD) is a fully data driven method for decomposing signals into a set of AM-FM components known as intrinsic mode functions (IMFs). Despite its usefulness in the analysis of real world signals, the process is rather deterministic and sensitive to parameters such as local envelope estimation. A combination of EMD and machine learning is proposed which provides an algorithm that is more robust to EMD parameters. In addition, the proposed extension is fully adaptive and facilitates the "data fusion via fission" mode of operation. The derivation and analysis of the proposed framework is supported with simulations in denoising and prediction applications.

引用

页码：1897 / 1900

页数：4

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