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- 2018
EMD端点效应抑制方法
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Abstract:
针对经验模态分解(empirical mode decomposition,简称EMD)中的端点效应问题,在研究总结了现有端点效应抑制方法的基础上,提出一种新的方法——基于支持向量机(support vector machine,简称SVM)延拓和窗函数相结合的方法,弥补了SVM延拓依然找不到端点以及窗函数会改变原始信号的缺点。首先,采用SVM对原始信号两端分别进行延拓,将延拓后的数据进行加窗处理(中间加矩形窗,延拓数据加海明窗);然后,利用EMD方法对加窗后的信号进行分解,得到若干个内禀模态函数(intrinsic mode function,简称IMF);最后,将IMF分量的两端延拓部分去掉,以此来达到抑制端点效应的目的。以正交性为量化评价指标,对比分析了不同方法的性能,通过仿真和实验结果表明,该方法可以更好地抑制端点效应的发生。
In order to restrain the end effect of EMD(empirical mode decomposition), a novel method that combines SVM (support vector machine) extension and window function is proposed. The combination makes up for the disadvantages that the SVM continuation fails to find the endpoint and window function could changes the original signal. First, the signal is extended at each extreme point by SVM and process the original data with a rectangular window and the continuation data with a hamming window. Second, employ EMD method to decompose the data and get a number of intrinsic mode functions(IMFs). Finally, the ends of the decomposed IMF component continuation section are removed in order to suppress the endpoint divergence. Orthogonality is chosen as the quantitative evaluation index. Simulation and experiments show that this method can effectively restrain the end effects during the process of empirical mode decomposition.