关于非对称数据的ESMD改进算法

Improvement of ESMD Algorithm for Asymmetric Data

摘要: ESMD方法是“极点对称模态分解方法”的简称，其在各个研究领域所涉及数据处理的科研和工程应用有其独特的作用。但是通过ESMD数据分析方法的原理可知，其一般适应于在每半个周期中关于中点较对称的数据。对于非对称(峰谷不对称)数据来说，该方法中选取的中点失去了代表性。本文针对这种数据的分析对ESMD方法进行了改进，用每半个周期的局部均值来代替原先算法中极大值与极小值的中点，这样可以更好的反应非对称数据的情况，并通过数值模拟验证了上述方法的可行性与有效性。

Abstract: The ESMD method is the abbreviation of “Extreme-point Symmetric Mode Decomposition method”. It has its unique role in the research and engineering application of data processing involved in various research fields. But according to the principle of ESMD data analysis method, it is generally suitable for the more symmetrical data about the midpoint in every half period. For asymmetric (peak-valley asymmetry) data, the midpoint selected in this method is unrepresentative. In this paper, the ESMD method is improved for the analysis of this kind of data, and the local mean value per half period is used to replace the midpoint of maximum and minimum in the original algorithm, so that the asymmetric data can be better reflected. The numerical simulation proves the method’s feasibility and effectiveness.