摘要
EMD分解是一种受信号本身时域局部特征驱动的自适应分析方法,能够把信号分解成少数几个具有物理意义的本征模态函数分量。本文总结归纳了一维EMD分解的主要工作,比较了不同方法存在的优点与不足,分析了EMD分解研究存在的难题和瓶颈,并探讨了EMD分解研究与应用的发展趋势。
Empirical Mode Decomposition( EMD) is a data-driven and self-adaptive decomposition algorithm which is used for time-frequency analysis. A review work about the current development of one dimensional EMD is presented. At first,some basic concepts and main algorithm ideas are introduced. Then the advantages and shortages of EMD are discussed. At the end of the paper,a few problems which are waiting to be solved are listed.
出处
《中山大学研究生学刊（自然科学与医学版）》
2015年第1期35-45,共11页
Journal of the Graduates Sun YAT-SEN University(Natural Sciences.Medicine)
