新型经验模式分解端点效应消除方法

对经验模式分解(Empirical mode decomposition, EMD)的端点效应进行深入分析，指出现有延拓方法的不足，对基于正弦函数延拓的方法进行改进，分析不同的延拓周期、延拓信号长度及信号端点值对EMD分解的影响，并与镜像延拓EMD分解方法进行了分析比较。在此基础上，提出一种基于指数正弦型延拓方法的EMD分解，它从原理上减小了延拓信号端点包络线的发散程度，从而提高了EMD分解精度和速度，并对其进行了仿真分析。研究结果表明，新的延拓方法优于传统的延拓方法，能较好地抑制EMD端点效应。

New Approach to Dealing with the End Effect of Empirical Mode Decomposition

The end effect of empirical mode decomposition that appears when using spline interpolation to get two envelops of the data is discussed. The shortage of ordinary end extending methods is pointed out. An extending method based on trigonometric functions is improved, and the influence of different extending periods, length and value of end point of the data is discussed. A comparison with mirror extending method is made and analyzed. On the basis of the improved method, a new method with exponent sine function extending approach is proposed, which decreases the envelop emanation of end points of extending data, so it can improve the speed and precision of EMD, and simulation is used to prove the effectiveness. Results show that the proposed improved extending method can restrain the end effect effectively.