摘 要:经验模式分解（EMD）是一种自适应的分解算法. 现实中采集到的信号总或多或少带有一些噪声成分. 通过对信号的经验模式分解进行分析，发现信号中包含的噪声对分解结果影响较大. 本文对于幅值较大的噪声，先利用小波变换的降噪功能，对信号进行小波阈值降噪，再和EMD分解相结合，能更准确的得到分析结果.-
关键词：经验模式分解；小波降噪；阈值降噪
Abstract:The empirical mode decomposition(EMD) is an adaptive method for signal processing. From the decomposition results of some familiar signals using EMD, it is found that the noise in the signal affected the results greatly. For amplitude larger noise, firstly, the noise of the signal is reduced in the form of wavelet value threshold by using wavelet transform noise reduction function, combining with EMD. In this way, more accurate results of analysis could be obtained.
Keywords: empirical mode decomposition; wavelet noise reduction; value threshold de-noising
目录
第一章 引言-1
第二章 经验模式分解-3
2.1 经验模式分解原理-3
2.2 EMD实例-6
第三章 小波降噪-8
第四章 实验信号加噪分析-10
第五章 结论-16
参考文献-17
致 谢-18
