Convergence of a Convolution-Filtering-Based Algorithm for Empirical Mode Decomposition
Convergence of a Convolution-Filtering-Based Algorithm for Empirical Mode Decomposition
复制标题
DOI：
10.1142/s1793536909000205
复制
发表时间：
2009-10
期刊：
影响因子：
--
通讯作者：
Chao Huang;Lihua Yang;Yang Wang
中科院分区：
文献类型：
--
作者：
Chao Huang;Lihua Yang;Yang Wang
Lin等人提出了迭代Toeplitz滤波算法作为经验模式分解（EMD）的替代迭代算法。在这种替代算法中，上下包络的平均值由通过低通滤波器获得的特定“移动平均值”代替。用这种移动平均值执行传统的筛选算法相当于迭代某些卷积滤波器（有限长度Toeplitz滤波器）。本文研究了该算法对连续变量信号的收敛性，证明了该迭代算法的极限函数是一个理想的高通滤波过程。
Lin et al. propose the iterative Toeplitz filters algorithm as an alternative iterative algorithm for Empirical Mode Decomposition (EMD). In this alternative algorithm, the average of the upper and lower envelopes is replaced by certain "moving average" obtained through a low-pass filter. Performing the traditional sifting algorithm with such moving averages is equivalent to iterating certain convolution filters (finite length Toeplitz filters). This paper studies the convergence of this algorithm for signals of continuous variables, and proves that the limit function of this iterative algorithm is an ideal high-pass filtering process.