Application of Empirical Mode Decomposition Based on Kernel Principal Component Analysis

Wang Lei;Wang Feng-tao;Zhu Hong;Zhang Zhi-xin;Guo Zheng-gang

Journal of Vibration and Shock ›› 2010, Vol. 29 ›› Issue (2) : 39-41,6.

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PDF(1272 KB)
Journal of Vibration and Shock ›› 2010, Vol. 29 ›› Issue (2) : 39-41,6.
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Application of Empirical Mode Decomposition Based on Kernel Principal Component Analysis

  • Wang Lei;Wang Feng-tao;Zhu Hong;Zhang Zhi-xin;Guo Zheng-gang
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Abstract


If the signal-noise ratio is smaller, the correct intrinsic mode function(IMF) can not be decomposed by empirical mode decomposition(EMD), which may be a mistake IMF. According to this case, kernel principal component analysis (KPCA) and EMD method would like to be combined. Firstly, signal phase-space is founded , the kernel principal component of signal phase-space is extracted by KPCA, and then the phase-space is reconstructed by using the adopted kernel principal component through the inverse process. Finally, the reconstructed signal is decomposed by EMD method. The noise impact on EMD can be effectively eliminated by this way, so the decomposition ability of EMD is improved to ensure the validity of EMD in all cases and the correct IMF would be broken down. The effectiveness and feasibility of this method is verified further by actual works.

Key words

kernel principal component analysis / empirical mode decomposition / signal processing

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Wang Lei;Wang Feng-tao;Zhu Hong;Zhang Zhi-xin;Guo Zheng-gang. Application of Empirical Mode Decomposition Based on Kernel Principal Component Analysis[J]. Journal of Vibration and Shock, 2010, 29(2): 39-41,6
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