Adaptive KEEMD and Its Application in Spectrum Denoising

Ensemble empirical mode decomposition( EEMD) overcomes the mode mixing problem of EMD,however the end effect still exists. Additionally,two key parameters in EEMD: the amplitude of added noise and the number of ensemble times are set up by experience,not conducive to complete the signal decomposition fast and accurately. To solve the above problems,a method of adaptive KEEMD was proposed in this paper. First,the end effect was restraimed through an extrema extension based on the kernel extreme learning machine( KELM) and mirror extension,then it was applied to the simulate signal decomposition and wheat reflectance spectrum denoising,and the effectiveness of restraining end effect was verified by the result. Second,through the high frequency component of signal obtained by above step,the two adaptive parameters in KEEMD was obtained. The adaptive method was used to cole and spinach reflectance spectrum denoising.Resultsshow that denoising results can be obtained more quickly and be consistent with the non-adaptive method.