Abstract
Blind source separation is a hot topic in signal processing. Most existing works focus on dealing with linear combined signals, while in practice we always encounter with nonlinear mixed signals. To address the problem of nonlinear source separation, in this paper we propose a novel algorithm using radial basis function neutral network, optimized by multi-universe parallel quantum genetic algorithm. Experiments show the efficiency of the proposed method.
Highlights
The nonlinear mixed signals are widespread in blind source separation(BSS), which traditional methods based on linear mixture assumption like ICA are unable to deal with
According to [1,10-12], supposing that x, y are two independent random variables, and f, g are two nonlinear functions, f(x) and g(y) are independent, too. This fact means that the nonlinear function of source signals could possibly be recovered with the independence assumption of source signals. [2] discusses the existence and uniqueness of solutions to nonlinear ICA and concludes that the solution exists but more assumptions should be added to confirm the uniqueness of solution
We start to optimize radial basis function neutral network (RBFNN) optimized by multiuniverse parallel quantum genetic algorithm (MPQGA) iteratively
Summary
The nonlinear mixed signals are widespread in blind source separation(BSS), which traditional methods based on linear mixture assumption like ICA are unable to deal with. According to [1,10-12], supposing that x, y are two independent random variables, and f, g are two nonlinear functions, f(x) and g(y) are independent, too. RBFNN does not deed to determine the mixture model. It is unsupervised and can approximate an arbitrary function [4,5]. RBFNN always converges to a local minimum by adopting general optimization method, such as the gradient descent algorithm.
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