Blind Source Separation Research Based on the Feature Distance Using Evolutionary Algorithms

Blind Source Separation Research Based on the Feature Distance Using Evolutionary Algorithms

Without any information on the mixing system, the blind source separation (BSS) technique efficiently separates mixed signals. The approach called evolutionary algorithms was used for the BSS problem in this paper. The fitness function based on the feature distance and kurtosis was proposed to measure the degree of the separated signals in this paper. Compared with the traditional algorithm in ...

Novel blind source separation algorithms using cumulants

Robust blind source separation algorithms using cumulants

In this paper we propose a new approach to blind separation of independent source signals that, while avoiding the imposition of an orthogonal mixing matrix, is robust with respect to the existence of additive Gaussian noise in the mixture. We demonstrate that, for the wide class of source distributions with certain non-null cumulants and a pre-speci3ed scaling, separation is always a saddle po...

Nonlinear Blind Source Separation Using Genetic Algorithms

This article proposes the fusion of two important paradigms, Genetic Algorithms and the Blind Separation of Sources (GABSS). Although the topic of BSS, by means of various techniques, including ICA, PCA and neural networks, has been amply discussed in the literature, to date the possibility of using genetic algorithms has not been explored. However, in nonlinear mixtures, optimisation of the sy...

Comparison of Blind Source Separation Methods based on Iterative Algorithms

In this paper some approaches to detect signal streams of a multi layer transmission system are presented. We will focus on blind algorithms for the separation of the data stream and improve their performance in an iterative way in order to gain nearly the same performance as with a known channel matrix. The overall algorithm will remain blind and does not need any training data.