一种新的基于归一化峭度的ICA算法

广东工业大学学报 ›› 2007, Vol. 24 ›› Issue (4): 38-41.

一种新的基于归一化峭度的ICA算法

广东工业大学应用数学学院；广东工业大学应用数学学院广东广州510006；广东广州510006；

出版日期:2007-11-08 发布日期:2007-11-08

A New ICA Algorithm Based on Normalized Kurtosis

(Faculty of Applied Mathematics,Guangdong University of Technology,Guangzhou 510006,China)

Online:2007-11-08 Published:2007-11-08

摘要/Abstract

摘要： 基于峭度的独立分量分析中,采用梯度法寻找代价函数的极值点时,搜索的方向对算法的收敛起着关键的作用,对Kuicnet算法中原来的梯度做一个倍数替换,得到一个新的ICA算法.新算法在迭代过程中适时对梯度方向作出调整,加快收敛速度.数值仿真说明了算法的有效性. 更多还原

关键词: 独立分量分析；峭度；快速ICA算法.；

Abstract: The traditional approach to gradient to search for the cost function’s extreme in the independent component analysis was based on kurtosis.The path we are looking for is crucial to the convergence of the algorithm.In the Kuicnet algorithm,after replacing the gradient of the cost function with its scaling we obtained a new ICA algorithm.Adjusting the path of the gradient timely in the iterative process,it accelerates the convergence.The simulation illustrates the effectiveness.

Key words: independent component analysis； kurtosis； fast-ICA algorithm；

崔志涛；温洁嫦；胡南辉； . 一种新的基于归一化峭度的ICA算法[J]. 广东工业大学学报, 2007, 24(4): 38-41.

CUI Zhi-tao,WEN Jie-chang,HU Nan-hui . A New ICA Algorithm Based on Normalized Kurtosis[J]. Journal of Guangdong University of Technology, 2007, 24(4): 38-41.

参考文献

[1] DOUGLAS S C,KUNG S Y.Kuicnet algorithms for blinddecon-volution. IEEE Workshop Neural Networks Sig-nal Process Cambridge . 1998

[2] HYVARINEN A,OJA E.Independent Component Analy-sis:Algorithms and Application. Neural Networks Cen-tre . 2000

[3] GEORGIE P,CICHOCKI A,AMARI S.On some exten-sions of the natural gradient algorithm. Third Interna-tional Conference on Independent Component Analysis andBlind Signal Separation(ICA 2001) . 2001

[4] HYVARINEN A,KARHUNEN J,OJA E.Independentcomponent analysis. . 2001

[5] JUTTEN C.Blind separation of sources. Signal Processing . 1991

[6] COMON P.Independent component analysis,a new con-cept. Signal Processing . 1994

[7] Lee T W.Independent component analysis using an extended infomax algorithm for mixed subgaussion and supergaussion sources. Neural Computation . 1999

[8] Hyv rinen A.Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks . 1999

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