Empirical mode decomposition based on the kernel extreme learning machine and its application in the spectrum denoising

Empirical mode decomposition (EMD) is a time-frequency analysis method for non-stationary and nonlinear signal. The EMD decomposes signal into a collection of intrinsic mode functions (IMFs), which can highlight the local characteristics of the original signal and discriminate the signal from the noise. However the method has a problem of end effect. The problem makes the IMFs polluted in the sifting process of EMD, and some feature information of the original signal is lost after reconstruction. In order to mitigate this problem, an extreme point expansion method based on kernel extreme learning machine (KELM) is proposed in this paper. The proposed method is utilized to predict several extreme points at both ends of the original signal, improve the accuracy of sifting process, increase the effectiveness of the reconstructed signal. In case of compared with the other traditional methods, firstly, the proposed method is verified by the simulate signal decomposition, simulation results demonstrate the method can restrain end effect effectively and improve the decomposition results of EMD; secondly, the proposed method is applied to denoising wheat canopy reflectance spectrum and the result shows that the method can offer better signal-to-noise ratio (SNR) and root mean square error (RMSE), remove noise from the original signal accurately and effectively.