用于解决EMD分解过程中存在的模态混叠问题，采用总体平均经验模态分解算法，同传统EMD相比，分解效果更好。(To solve the problem of modal aliasing existing in the EMD decomposition process, the total average empirical mode decomposition (EMD) algorithm is adopted, and the decomposition effect is better

CEEMDAN方法，用于解决EMD分解过程中的模态混叠问题，同常用的EEMD方法相比，其有效减少了迭代次数，增加了重构精度，更加适合非线性信号的分析。(CEEMDAN method is used to solve the modal aliasing problem in the EMD decomposition process. Compared with the commonly used EEMD method, it effectively reduces the number of

经验模态分解EMD 集合经验模态分解EEMD matlab程序，调试可用，可以在此基础上进行改进