摘要
经验模态分解是Hilbert-Huang变换(HHT)的关键算法,它分解信号的能力直接影响到HHT的实用性和应用价值.为了研究经验模态分解对多分量复合信号的筛选条件,我们主要研究了在理想条件下经验模态分解筛选过程的特性及其对双分量信号模型的筛选条件,然后推出经验模态分解对多分量复合信号的筛选条件,并给出相应数值实验分析.
Empirical Mode Decomposition is the key arithmetic in the Hilbert-Huang Transform （HHT）, the competence of its decomposing signal is affecting directly the practicability and application value of HHT. In order to study the sifting condition of the multi-component signal for the Empirical Mode Decomposition, we principally analyzed the characteristic of sifting process in the ideal condition for the Empirical Mode Decomposition and its sifting condition for the double-component model, and then we can deduce the sifting condition of multicomponent signal for the Empirical Mode Decomposition, also provide it with corresponding numerical experiment to analyze.
出处
《南阳师范学院学报》
CAS
2010年第3期22-26,共5页
Journal of Nanyang Normal University
基金
国家自然科技基金项目(10871217)
关键词
经验模态分解
固有模态函数
包络曲线
筛选过程
Empirical Mode Decomposition
intrinsic mode function
envelope curve
sifting process
作者简介
全学海（1984-），广西桂林人，硕士研究生，主要从事小波分析与希尔伯特-黄变换在信号处理中的应用研究．
