经验模态分解是Huang等人在1998年提出的，该方法的本质是将一个非

Empirical mode decomposition was proposed by Huang et al. in 1998. The essence of this method is to gradually decompose a non-smooth time series into fluctuations or trends of different scales, so as to obtain a series of data series with different feature scales. Each sequence is called an eigenmode function, and the trend or mean of the original signal is usually the lowest screen IMF component.

Empirical modal decomposition was proposed by Huang et al. in 1998, and the essence of this method is to gradually break down a non-smooth time series into fluctuations or trends of different scales, so as to obtain a series of data series of different characteristic scales. Each sequence is called a character mode function, and the trend or mean of the original signal is usually the IMF component on the lowest screen.

Empirical mode decomposition (EMD) was proposed by Huang et al in 1998. The essence of this method is to decompose a non smooth time series into different scale fluctuations or trends gradually, so as to obtain a series of data series with different characteristic scales. Each sequence is called eigenmode function, and the trend or mean value of the original signal is usually the IMF component of the lowest screen.<br>