Independent component analysis algorithm research based on improved particle swarm
LI Qiao-yan
QUAN Hai-yan
Abstract：In order to solve the problems such as easy falling into local optimum particle and slow convergence speed in traditional particle swarm optimization(PSO)algorithm,an independent component analysis(ICA) algorithm based on the improved PSO algorithm was proposed.The method chose the value of the inertia weight factor ωrandomly in the section to make the particle have adaptive ability.Because of this,the improved PSO algorithm could search the optical particle quickly.Meanwhile,it used the mutual information in ICA as the objective function,and the improved PSO algorithm to optimize the objective function,which made the compo-nents to be independent among each other.Simulation results showed the proposed method inproved the global search ability,could separate the mixed signal effectively and improved the result of the blind source separation.
Keywords：independent component analysis(ICA)particle swarm optimization (PSO)algorithmadaptive adjustmentmutual information
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 103-108 )