【摘要】 经验模态分解(EMD)作为时频分析的经典算法,已经得到广泛的应用。然而,其分解质量容易受到噪声等干扰的影响,产生模态混叠问题。本文针对经验模态分解中因噪声存在的模态混叠问题,提出一种自适应的预处理方法。首先对输入信号进行B样条最小二乘拟合,消除了噪声的影响后,再进行EMD分解。为提高算法的自适应性,提出了一种基于极值点出现时刻的节点选取方法。对线性信号与非线性信号的仿真实验表明该方法有较高的分解精度;与聚合经验模态分解方法(EEMD)的分析对比结果表明该方法能很好地抑制噪声引起的模态混叠。更多还原

【Abstract】 Empirical mode decomposition has become an established tool for time-frequency analysis and has been widely used. However,a major problem is that its performance of EMD may be affected by intermittence or noise,known as the mode-mixing problem. In order to overcome the mode-mixing problem in the empirical mode decomposition( EMD) algorithm,an adaptive pre-processing technique is proposed. In this work,B-spline least squares approximation is first studied and employed before the use of EMD to eliminate the noise which may result in mode mixing. After that,a knot placement iteration algorithm using the extrema time location is put forward to enhance the adaptive property of the proposed method.Simulations of linear and non-linear signals show that it is capable of significantly reducing mode-mixing problem caused by noise. Comparisons between the proposed method and EEMD method are carried out,indicating that the proposed method is superior to existing methods in accuracy.更多还原