Open AccessA chaotic signal denoising method developed on the basis of noise-assisted nonuniformly sampled bivariate empirical mode decomposition
Author(s) -
Xiaofei Wang,
Jian Qu,
Feng Gao,
Zhou Yu-Ping,
Xiangyu Zhang
Publication year - 2014
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.63.170203
Subject(s) - hilbert–huang transform , chaotic , bivariate analysis , noise (video) , noise reduction , gaussian noise , white noise , attractor , mode (computer interface) , energy (signal processing) , signal (programming language) , mathematics , computer science , algorithm , artificial intelligence , statistics , mathematical analysis , image (mathematics) , programming language , operating system
According to the advantages of nonuniformly sampled bivariate empirical mode decomposition and the characteristics of noise after it, an adaptive chaotic signal denoising method is proposed based on the noise-assisted nonuniformly sampled bivariate empirical mode decomposition. Firstly, a complex signal is constructed for the noise-assisted nonuniformly sampled bivariate empirical mode decomposition, by using noisy chaotic signal and gaussian white noise as the real part and imaginary part respectively; secondly, the noise energy of each intrinsic mode function in the real part is estimated according to the energy of each intrinsic mode function in the imaginary part; and finally, from the above results, each intrinsic mode function in the real part is denoised by using the singular value decomposition. Noise energy estimate numerical experiment validates the feasibility of this method, and the denoising tests for Lorenz signal and monthly sunspot data indicate that our method shows advantages in both noise reduction and chaotic attractor topological configuration reversion.