EMD新算法及其应用

Novel EMD Algorithm and Its Application

经验模态分解(EMD)算法是 Hilbert-Huang 变换(HHT)的核心算法,它的分解效果依赖于包络线的生成算法和端点延拓算法。采用分段幂函数插值算法求包络线,结合一种改进的端点延拓算法,得到了一种新的 EMD 算法。分析了分段幂函数插值算法的收敛精度,从数学角度解释了选取该插值算法的原因。最后,结合一个股票模型的仿真结果说明新的 EMD 算法效果更好。

Empirical mode decomposition （EMD）, the core of Hilbert-Huang transformation, is reckoned on the algorithm of the extrama extending and generation of envelope curves. With the piecewise power function and an improved algorithm of extrama extending, a novel EMD algorithm was proposed. An error estimation of the piecewise power function interpolation was proposed to show the advantage of this algorithm. At last, a simulation on a stock model is presented.to show that this novel EMD algorithm is more applicable to analyze the nonlinear and non-stationary signal than the previous EMD algorithm.