Blind source separation of continuous-time chaotic signals based on fast random search algorithm

Authors:
Zangbo Xie;Jiuchao Feng
Affiliations:
School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China;School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
Venue:
IEEE Transactions on Circuits and Systems II: Express Briefs
Year:
2010

Citing 5
Cited 0

A modified BFGS method and its global convergence in nonconvex minimization

Journal of Computational and Applied Mathematics - Special issue on nonlinear programming and variational inequalities
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
On Accelerated Random Search

SIAM Journal on Optimization
Topographic Independent Component Analysis

Neural Computation
A globally convergent BFGS method with nonmonotone line search for non-convex minimization

Journal of Computational and Applied Mathematics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Blind source separation (BSS) of continuous-time chaotic signals from a linear mixture is addressed in this brief. It is assumed that the functional forms of the generating systems of chaotic signals are known, and the parameters of the generating systems and the mixture matrix are unknown. The problem of determining the parameters and the mixture matrix is formulated as an optimization one. A fast random search (FRS) algorithm is, therefore, proposed. Experimental results demonstrate that the FRS algorithm can solve the indeterminacy problem in BSS and show the separability of mixed signals in a high noise background.