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An approach of identifying the parameters of IMFs based on PLF

机译:基于PLF识别IMFS参数的方法

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While identifying the parameters of IMFs from Empirical Mode Decomposition, by Hilbert-Huang Transform, a piece of approximately linear data segment is necessary for a specific result. The select of the data segment will directly influence accuracy of the parameters. The time for getting the approximately linear data segment is required to be as short as possible. The paper uses Least Square Series-piecewise Linear Fitting method to divide data into pieces, then chooses several pieces with the highest goodness-of-fit, and takes each median as basis to change the length, for higher goodness-of-fit. The needed data segment is achieved in the case that this data segment can still reflect the inherent parameters. This paper brings some examples to verify that the approach is feasible and exact.
机译:在识别来自经验模式分解的IMF的参数,通过Hilbert-Huang变换,特定结果需要一段大约线性数据段。选择数据段将直接影响参数的准确性。获取近似线性数据段的时间是尽可能短的。本文使用最小二乘系列 - 分段的线性拟合方法将数据分成碎片,然后选择几件,具有最高的适合度,并将每个中位数作为改变长度,以实现更高的高度。在该数据段仍然可以反映固有参数的情况下实现所需的数据段。本文带来了一些示例,以验证该方法是可行和精确的。

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