Blind source separation based on Bayesian optimization algorithm with decision graphs
Yunru Liu · Computer Engineering and Applications Journal · 2010
Recovering the unobserved source signals from their mixtures is a typical problem in array processing and data analysis.In this paper,a blind source separation algorithm using Bayesian optimization algorithm with decision graphs is proposed,which uses Bayesian optimization algorithm with decision graphs instead of the joint diagonalization operation in JADE to improve the accurateness of the solutions.The suggested algorithm replaces some genetic operators such as crossover and mutation in traditional genetic algorithms by building and learning Bayesian networks,which avoids setting a lot of parameters manually and destroying some important building blocks.The analysis and simulations suggest that the algorithm has a higher separation accuracy than JADE algorithm and blind source separation based on GA.