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Improving the empirical mode decomposition method

机译:改进经验模式分解方法

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In recent years, the analysis of non-stationary signals has taken on great importance since the introduction of empirical mode decomposition (EMD) by Huang in 1998. The algorithm is based entirely on a discrete algorithm, meaning that, for now, no clear analytical interpretation exists. In this article, we study several aspects of the EMD algorithm in order to improve the decomposition. On the one hand, the sampling frequency must be optimized so as to maximize the similarity between the discrete and continuous signals, minimizing the computational cost required to apply analysis methods for non-linear and non-stationary signals. On the other hand, a solution to border effect which gives good results for signals of approximately constant, growing or decreasing amplitude near the borders is provided. Moreover, the stopping criteria must be modified to limit the amplitudes allowed to IMF. Some examples are shown at the end.
机译:自从1998年Huang引入经验模态分解(EMD)以来,非平稳信号的分析就变得非常重要。该算法完全基于离散算法,这意味着到目前为止,尚无清晰的分析方法。存在解释。在本文中,我们研究了EMD算法的几个方面,以改进分解。一方面,必须优化采样频率,以使离散信号和连续信号之间的相似度最大化,从而使对非线性和非平稳信号应用分析方法所需的计算成本最小化。另一方面,提供了一种对边界效应的解决方案,该解决方案对于在边界附近具有近似恒定,增大或减小的幅度的信号给出了良好的结果。此外,必须修改停止标准以限制允许IMF的振幅。最后显示一些示例。

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