Methods for Mitigation of End Effect in Empirical Mode Decomposition: A Quantitative Comparison
经验模态分解中多种边界处理方法的比较研究

One of the most important problems in Empirical Mode Decomposition (EMD) applications is mitigation of the end effect. Except Huang's patented approach several methods have been proposed. However, a final solution for this problem is yet to be found. In this paper five common end effect mitigation methods of EMD have been investigated, including linear extending method, polynomial fitting extending method, mirror extrema extending method, RBF neural network prediction method and AR prediction method. With a quasi-periodical signal and a stochastic signal as the test bed a quantitative test method was proposed for elimination of the mode confusion effect of EMD. The five end effect mitigation methods were quantitatively evaluated and the comparison shows that mirror extrema extending method is the best option among the five methods.