中文摘要：

总体经验模态分解（EEMD）是一种基于多次经验模态分解（EMD）的信号分解方法,能够有效解决EMD方法中存在的模态混.叠问题,使得分解出来的分量更具有实际物理意义,但随之带来的问题是整个分解时间大大延长,这正是由于多次EMD分解造成的;为了能够同时提高EEMD方法的分解效率,提出了一种基于小波预处理的EEMD算法;通过实验结果可以发现改进后的EEMD算法在原始EEMD算法的基础上,分解效率平均提高了大约10.95%,得到的有效分量与原始信号的相关度也提高了大约8.68%;这就说明较原有EEMD算法相比,改进后的EEMD算法不仅能够提高信号分解速度,而且能够更为有效地提取出原始信号中的特征分量。

英文摘要：

As a method based on many times empirical mode decomposition （EMD）, ensemble empirical mode decomposition （EEMD） can solve the mode mixing problem effectively and make the IMFs have more physical meaning. But the attended problem is the greatly ex tended decomposition time, which is caused by the many times EMD. To effectively improve decomposition efficiency of the EEMD algo rithm, an improved EEMD algorithm based on wavelet pretreatment is proposed in this paper. Simulation experiments show that, the decom position efficiency increases about 10.95% and the correlation between the effective component and the original signal increased about 8. 68%. So, compared with the original EEMD algorithm, the proposed algorithm can get higher decomposition efficiency and extract more effective characteristic components.