基于差分和局部积分均值的EMD算法

Empirical Mode Decomposition Algorithm Based on Difference and Local Integral Mean

摘要:

针对经验模态分解中无法分离频率相近信号的问题,提出一种新的经验模态分解算法。该算法将局部积分均值的“筛分”算法和差分经验模态分解算法的思想结合在一起。仿真结果表明,该算法的频率分辨率比基于局部积分均值的经验模态分解算法要高,分解效率要比基于差分的经验模态分解算法要高,同时在一定程度上抑制了边界效应。

Abstract:

In order to solve the problem of mode mixing in empirical mode decomposition (EMD), an algorithm was proposed based on the difference operation and LIM-EMD. The algorithm combined the two algorithms. The simulation results shown that the proposed algorithm that relative to the algorithm of empirical mode decomposition based on local integral mean, improving the frequency resolving power. The proposed algorithm that relative to the algorithm of empirical mode decomposition based on the difference operation, considerably accelerates the convergence of the sifting iteration and remarkably eliminated end effect.