Chaotic signal denoising algorithm based on sparse decomposition*

Abstract

Denoising of chaotic signal is a challenge work due to its wide-band and noise-like characteristics. The algorithm should make the denoised signal have a high signal to noise ratio and retain the chaotic characteristics. We propose a denoising method of chaotic signals based on sparse decomposition and K-singular value decomposition (K-SVD) optimization. The observed signal is divided into segments and decomposed sparsely. The over-complete atomic library is constructed according to the differential equation of chaotic signals. The orthogonal matching pursuit algorithm is used to search the optimal matching atom. The atoms and coefficients are further processed to obtain the globally optimal atoms and coefficients by K-SVD. The simulation results show that the denoised signals have a higher signal to noise ratio and better preserve the chaotic characteristics.

Effect of astrocyte on synchronization of thermosensitive neuron-astrocyte minimum system

“…[4][5][6] Obviously, the global dynamic behavior of the system is very complex, [7][8][9][10][11] and is subject to many factors, such as network scale, [12,13] initial value sensitivity, [14,15] and system noise. [16][17][18][19] Therefore, it is very difficult to regulate the complex network. Based on the medical significance and theoretical value of neural regulation, the study of nonlinear dynamic characteristics of neuronal discharge has attracted increasing attention recently.…”