An Adaptive Denoising Algorithm for Chaotic Signals Based on Improved Empirical Mode Decomposition

摘要

It is difficult to determine the threshold of mode cell in the interval-thresholding algorithm, when it is used to denoise chaotic signals. In this paper, an adaptive denoising algorithm is proposed for chaotic signals based on improved empirical mode decomposition. First, the noisy chaotic signal is decomposed into the intrinsic mode functions (IMFs) by improved complete ensemble empirical mode decomposition. Then, the zero-crossing scale thresholding denoising algorithm is used to denoise the IMFs with different thresholds. The optimal threshold is obtained by the Durbin-Watson criterion. With the optimal threshold, the final denoised chaotic signal is obtained. The proposed algorithm effectively solves the issue mentioned above. The experimental results show the proposed algorithm can denoise noisy chaotic signals in different conditions effectively and is better than other existing algorithms.