END EFFECTS MITIGATION FOR EMPIRICAL MODE DECOMPOSITION WITH NONLINEAR GRAY MODEL
Abstract
To mitigate end effects of empirical mode decomposition (EMD), a novel approach inspired by the nonlinear gray model (GM) termed as GM(1,1,α) is presented. Other than traditional linear or mirror extension on the boundary, the GM(1,1,α) model is applied to predict two extrema at both ends of the data. It is worth noting that our GM(1,1,α) model is particularly useful for predicting uncertainty data. According to numerical experiments on synthetic signal as well as real data series, the proposed method gives very comparable results with other three generally acknowledged methods, including the linear extension (LE), window function (WF), and mirror symmetry (MS) based methods. That is, the proposed method can reduce end effects and improve decomposition results of EMD significantly.
