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Application of support vector regression machines to the processing of end effects of Hilbert-Huang transform

机译：支持向量回归机在希尔伯特-黄变换最终结果处理中的应用

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摘要

The end effects of Hilbert-Huang transform are represented in two aspects. On the one hand, the end effects occur when the signal is decomposed by empirical mode decomposition (EMD) method. On the other hand, the end effects occur again while the Hilbert transforms are applied to the intrinsic mode functions (IMFs). To restrain the end effects of Hilbert-Huang transform, the support vector regression machines are used to predict the signals before the signal is decomposed by EMD method, thus the end effects could be restrained effectively and the IMFs with certain physical sense could be obtained. For the same purpose, the support vector regression machines are used again to predict the IMFs before the Hilbert transform of the IMFs, thus the accurate instantaneous frequencies and amplitudes could be obtained and the corresponding Hilbert spectrum with physical sense could be acquired. The analysis results from the simulation and experimental signals demonstrate that the end effects of Hilbert-Huang transform could be resolved effectively by the time series forecasting method based on support vector regression machines which is superior to that based on neural networks.
机译：Hilbert-Huang变换的最终效果体现在两个方面。一方面，当通过经验模式分解（EMD）方法分解信号时，会产生最终效果。另一方面，在将希尔伯特变换应用于本征模式函数（IMF）时，最终效果再次出现。为了抑制Hilbert-Huang变换的端效应，利用支持向量回归机对信号进行EMD分解前的信号预测，可以有效地抑制端效应，得到具有一定物理意义的IMF。出于相同的目的，在IMF的希尔伯特变换之前，再次使用支持向量回归机预测IMF，从而可以获得准确的瞬时频率和幅度，并获得具有物理意义的相应希尔伯特频谱。仿真和实验信号的分析结果表明，基于支持向量回归机的时间序列预测方法优于基于神经网络的时间序列预测方法，可以有效地解决希尔伯特-黄变换的最终结果。

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