JOURNAL OF LIGHT INDUSTRY

CN 41-1437/TS  ISSN 2096-1553

Volume 31 Issue 2
March 2016
Article Contents
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 shu

Independent component analysis algorithm research based on improved particle swarm

  • Received Date: 2015-05-11
    Available Online: 2016-03-15
  • 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.
  • 加载中
    1. [1]

      JUTTEN C,HERAULT J.Blind of sources(Part I):an adaptive algorithm based on neuromimetic architecture[J].Signal processing,1991,24(1):1.

    2. [2]

      张银雪,王学民.基于改进PSO-ICA的地震信号去噪方法[J].石油地球物理勘探,2012,47(1):56.

    3. [3]

      马建仓,牛亦龙,陈海洋.盲信号处理[M].北京:国防工业出版社,2006:1-7.

    4. [4]

      孙路路.基于ICA的混合图像盲分离算法研究[D].南京:南京邮电大学,2010.

    5. [5]

      LEE T W,GIROLAMI M,SEJNOWSKI T J.Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources[J].Neural computation,1999,11(2):417.

    6. [6]

      HYVARINEN A,OJA E.A fast fixed-point algorithm for independent component analysis[J].Neural computation,1997,9(7):1483.

    7. [7]

      李刚磊.基于改进粒子群的ICA算法[J].科技信息,2011(26):81.

    8. [8]

      KENNEDY J,EBERHART R.Particle swarm optimization[C]//Proceedings of IEEE International Conference on Neural Networks,Piscataway:IEEE,1995(4):1942.

    9. [9]

      张文希,郑茂.基于粒子群优化的独立分量分析算法研究[J].科学技术与工程,2010,10(8):1866.

    10. [10]

      杨福生,洪波.独立分量分析的原理与应用[M].北京:清华大学出版社,2006:26-27.

    11. [11]

      REJU V G,KOH S N,SOON I Y.Partial separation method for solving permutation problem in frequency domain blind source separation of speech signals[J].Neurocomputing,2008,71(10/12):2098.

    12. [12]

      李丽,牛奔.粒子群优化算法[M].北京:冶金工业出版社,2009:27-29.

Article Metrics

Article views(1061) PDF downloads(39) Cited by()

Ralated
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return