For the purpose of reducing noise from grain flow signal, this paper proposes a filtering\nmethod that is on the basis of empirical mode decomposition (EMD) and artificial bee colony (ABC)\nalgorithm. At first, decomposing noise signal is performed adaptively into intrinsic mode functions\n(IMFs). Then, ABC algorithm is utilized to determine a proper threshold shrinking IMF coefficients\ninstead of traditional threshold function. Furthermore, a neighborhood search strategy is introduced\ninto ABC algorithm to balance its exploration and exploitation ability. Simulation experiments\nare conducted on four benchmark signals, and a comparative study for the proposed method and\nstate-of-the-art methods are carried out. The compared results demonstrate that signal to noise ratio\n(SNR) and root mean square error (RMSE) are obtained by the proposed method. The conduction of\nwhich is finished on actual grain flow signal that is with noise for the demonstration of the effect in\nactual practice.
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