经验模态分解(EMD)算法是通过算法过程定义的，而并非由确定的理论公式定义的，所以对其进行准确的理论分析非常困难，我们目前只能借助大量的数字仿真试验不断对其性能进行深入的研究。 EMD算法的目的在于将性能不好的信号分解为一组性能较好的本征模函数（IMFIntrinsic Mode Function ），且IMF须满足以下两个性质：
(1)信号的极值点(极大值或极小值)数目和过零点数目相等或最多相差一个；
(2)由局部极大值构成的上包络线和由局部极小值构成的下包络线的平均值为零。(Empirical Mode Decomposition (EMD) algorithm is defined by algorithmic process, rather than defined by the theoretical formula defined, so accurate theoretical analysis of it is very difficult, we can only rely on a large number of digital simulation experiments continue to its performance Conduct in-depth research. The purpose of the EMD algorithm is to decompose a signal with poor performance into a group of IMF Intrinsic Mode Functions, and the IMF must satisfy the following two properties:
(1) The number of extreme points (maximum or minimum) and the number of zero-crossing points of the signal are equal or at most differ by one;
(2) The average of the upper envelope composed of local maxima and the lower envelope composed of local minima is zero.)