摘要
经验模式分解(Empirical Mode Decomposition,EMD)是一种完全由数据驱动的自适应非线性时变信号分解方法,它将数据分解成具有物理意义的几个内蕴模式函数分量。介绍了一维EMD、二维EMD的基本概念、主要算法及其主要应用,指出了EMD的主要优点和缺点,给出了EMD研究与应用的发展趋势。
Empirical Mode Decomposition （EMD） is a decomposition algorithm which is used to analyze nonlinear and timevarying signal.Different from the traditional signal analysis method,the decomposition is data-driven and self-adaptive.A review work about the current development of one dimensional EMD and bidimensional EMD is introduced.At first,some basic concepts, main ＇algorithms trod applications are described.Then the advantages and shortages of EMD are discussed.At the end of the paper,several problems which are waiting to be solved are listed.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第4期120-124,共5页
Computer Engineering and Applications
基金
辽宁省自然科学基金No.20082176
浙江大学CAD&CG国家重点实验室开放基金(No.A0906)~~
关键词
经验模式分解
内蕴模式函数
非线性时变信号
Empirical Mode Decomposition（EMD）
Intrinsic Mode Function（IMF）
nonlinear and time-varying signal
