针对非线性非平稳信号的去噪问题，提出一种基于主成分分析（ＰＣＡ）的经验模态分解（ＥＭＤ）消噪方法．该方法根据ＥＭＤ的分解特性，利用ＰＣＡ对噪声信号经ＥＭＤ分解后的内蕴模态函数（ＩＭＦ）进行去噪处理-For nonlinear and non-stationary signal de-noising is proposed based on principal component analysis (PCA) of the empirical mode decomposition (EMD) de

其中一个为这经验模式分解的任务（EMD）是潜在有用的非参数信号去噪 为此小波阈值一直占主导地位的技术的区域多年。-One of the empirical mode decomposition of this task (EMD) is potentially useful non-parametric signal wavelet threshold de-noising for many years to this end has been the dominant technology i

利用EMD和小波信号去噪的参考文献，适合初学者学习！-The use of EMD and wavelet signal de-noising references, suitable for beginners to learn!

要求同学在学习EMD基本理论的基础上，对一维信号进行各种降噪方法的研究，实现信号去噪。 -EMD based learning requires students in the basic theory of one-dimensional signal noise study various methods to achieve signal de-noising.