摘要
由Huang提出的经验模态分解(Empirical Mode Decomposition,EMD)算法是一种数据驱动的自适应非线性时变信号分析方法,可以把数据分解成具有物理意义的少数几个固有模态函数(Intrinsic Mode Function,IMF)分量。然而模态混叠会导致错假的时频分布,使IMF失去物理意义,严重影响了EMD分解的准确性与实用性。分别针对一维和多维EMD抑制模态混叠,总结归纳了相关研究取得的主要成果,指出了各方法抑制效果的改进及仍有的不足。最后讨论了相关研究及应用未来的发展趋势。
The Empirical Mode Decomposition( EMD) algorithm proposed by Huang is a data driven adaptive analysis method for nonlinear time-varying signals. The signals can be decomposed into a few Intrinsic Mode Functions( IMFs) components with physical meaning. However, Mode Mixing( MM) can lead to wrong or false components in time frequency distributions, and then cause the decomposed IMFs losing their physical meaning. This seriously affects the EMD accuracies and applications. This study reviews methods of the MM suppression in one-dimensional and multi-dimensional EMD algorithms. The results improvements and limita-tions in related researches are summarized. Finally, the future development trend of related researches and applications are dis-cussed.
作者
戴婷
张榆锋
章克信
何冰冰
朱泓萱
张俊华
Dai Ting;Zhang Yufeng;Zhang Kexin;He Bingbing;Zhu Hongxuan;Zhang Junhua(Department of Electronic Engineering,Information School,Yunnan University,Kunming 650091,China;The Second Affiliated Hospital of Kunming Medical University,Kunming 650031,China)
出处
《电子技术应用》
2019年第3期7-12,共6页
Application of Electronic Technique
基金
国家自然科学基金(61561049
81771928)
关键词
经验模态分解
固有模态函数
模态混叠
HILBERT变换
empirical mode decomposition(EMD)
intrinsic mode function(IMF)
mode mixing(MM)
Hilbert transform
