LI Qiao-yan and QUAN Hai-yan. Independent component analysis algorithm research based on improved particle swarm[J]. Journal of Light Industry, 2016, 31(2): 103-108. doi: 10.3969/j.issn.2096-1553.2016.2.014
Citation:
LI Qiao-yan and QUAN Hai-yan. Independent component analysis algorithm research based on improved particle swarm[J]. Journal of Light Industry, 2016, 31(2): 103-108.
doi:
10.3969/j.issn.2096-1553.2016.2.014
Independent component analysis algorithm research based on improved particle swarm
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Institute of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
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Received Date:
2015-05-11
Available Online:
2016-03-15
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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 ω randomly 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 components 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.
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