两种抗模态混合的经验模态分解方法研究

2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Pub Date : 2013-07-23 DOI:10.1109/FSKD.2013.6816349

H. Hu, Wenlong Li, Feng Zhao

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引用次数: 3

摘要

经验模态分解(EMD)是在非平稳信号处理中发展起来的一种自适应分解方法。但EMD的主要缺点之一是当信号中的高频分量在时域中包含间歇性时，会出现模混。讨论了两种新的抗混模方法:频率外差EMD法和掩蔽信号EMD法。详细研究了这些方法的原理和操作步骤，并对它们进行了比较。在一个间隙非线性系统中的仿真和应用表明，这两种方法都能很好地解决常规EMD方法中的模态混合问题。

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Study on two mode-mixing resistant empirical Mode Decomposition methods

Empirical Mode Decomposition (EMD) is an adaptive decomposition method developed in non-stationary signal processing. But one of the main drawbacks of the EMD is the appearance of mode-mixing when high frequency components in a signal contain intermittence in time domain. The paper discusses two new mode-mixing resistant methods: frequency heterodyne EMD method and masking signal EMD method. The principle and the operating steps of these methods are studied in detail, as well as their comparison. The simulation and application in a backlash nonlinearity system shows that both of them can successfully solve the mode-mixing problem in normal EMD method.

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2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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