Inspection of Methods of Empirical Mode Decomposition

Roberto Hern'andez Santander, E. Casallas
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引用次数: 3

Abstract

Empirical Mode Decomposition is an adaptive and local tool that extracts underlying analytical components of a non-linear and non-stationary process, in turn, is the basis of Hilbert Huang transform, however, there are problems such as interfering modes or ensuring the orthogonality of decomposition. Three variants of the algorithm are evaluated, with different experimental parameters and on a set of 10 time series obtained from surface electromyography. Experimental results show that obtaining low error in reconstruction with the analytical signals obtained from a process is not a valid characteristic to ensure that the purpose of decomposition has been fulfilled (physical significance and no interference between modes), in addition, freedom must be generated in the iterative processes of decomposition so that it has consistency and does not generate biased information. This project was developed within the framework of the research group DIGITI of the Universidad Distrital Francisco Jose de Caldas.
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经验模态分解方法检验
经验模态分解是一种自适应和局部的工具,它提取非线性和非平稳过程的潜在分析成分,反过来,是Hilbert Huang变换的基础,然而,存在诸如干扰模式或确保分解的正交性等问题。用不同的实验参数和从表面肌电图获得的一组10个时间序列对算法的三种变体进行了评估。实验结果表明,对一个过程得到的解析信号进行重构时获得低误差并不是保证分解目的(物理意义和模间无干扰)得以实现的有效特征,而且在分解的迭代过程中必须产生自由度,使其具有一致性,不产生偏倚信息。该项目是在弗朗西斯科·何塞·德·卡尔达斯大学DIGITI研究小组的框架内开发的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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