[1]席志红,边峦剑,晋野.基于改进粒子群的盲源分离算法研究[J].应用科技,2010,37(01):12-14.[doi:10.3969/j.issn.1009-671X.2010.01.004]
 XI Zhi-hong,BIAN Luan-jian,JIN Ye.A novel blind source separation method based on improved particle swarm optimization[J].Applied science and technology,2010,37(01):12-14.[doi:10.3969/j.issn.1009-671X.2010.01.004]
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基于改进粒子群的盲源分离算法研究
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第37卷
期数:
2010年01期
页码:
12-14
栏目:
现代电子技术
出版日期:
2010-01-30

文章信息/Info

Title:
A novel blind source separation method based on improved particle swarm optimization
文章编号:
1009- 671X(2010)01- 0012- 04
作者:
席志红边峦剑晋野
哈尔滨工程大学 信息与通信工程学院,黑龙江 哈尔滨 150001
Author(s):
XI Zhi-hongBIAN Luan-jianJIN Ye
College of Information and Communication, Harbin Engineering University, Harbin 150001, China
关键词:
盲源分离独立分量分析预处理粒子群算法负熵
Keywords:
blind source separation independent component analysis pretreatment particle swarm optimization negentropy
分类号:
TN911.7
DOI:
10.3969/j.issn.1009-671X.2010.01.004
文献标志码:
A
摘要:
简要地介绍了盲源分离的基本理论,针对独立分量分析传统的优化算法易于陷入局部最优、收敛精度低的缺点,提出了一种基于改进型粒子群的盲源分离算法,将独立分量分析算法与改进的粒子群算法相结合,以负熵作为目标函数.采用这种改进的粒子群算法对分离矩阵进行调整使各个信号分量之间独立,完成对瞬时混合信号的盲分离.实验信号的分离仿真结果表明,该算法能够有效地完成混叠信号的分离.同时,在与传统的盲源分离算法进行对比中,体现出了更高的分离精度和稳定的性能.
Abstract:
The basic theory of blind source separation is introduced briefly. Traditional optimization algorithm carried out by independent component analysis method is easy to fall into partial optimum value, and the convergence precision is low. In view of these disadvantages, a blind source separation method based on an improved algorithm is put forward. It combines the independent component analysis algorithm and the improved particle swarm optimization algorithm, adopts negentropy as the target function, and optimizes the separation matrix by the improved particle swarm optimization algorithm so as to make each signal component independent, and therefore accomplishes blind source separation of the instantaneous mixed signals. The simulation result indicates that the improved algorithm can effectively separate the mixed signals. And compared with the traditional blind source separation algorithm, the improved algorithm represents higher separation precision and stable performance.

参考文献/References:

[1]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.
[2]TAN Y,WANG J. Nonlinear blind source separation using higher order statistics and a genetic algorithm[J]. IEEE Trans On Evolutionary Computation,2001,3:600-611.
[3]肖瑞.基于独立分量分析的盲源分离方法的研究[D].西安:西安理工大学,2007.
[4]HYVARINEN A, KARHUNEN J, OJA E. Independent componen analysis[M]. New York: John Wiley & Sons Inc,2001.
[5]郭武,朱长仁,王润生.一种改进的FastICA算法及其应用[J]. 计算机应用, 2008,28(4):960-962.
[6]刘丽芳.粒子群算法的改进及应用[D]. 太原:太原理工大学,2008.

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[1]王 雪,张兴周,田金超.基于峭度的盲源分离方法研究[J].应用科技,2006,33(06):32.
 WANG Xue,ZHANG X ing-zhou,T IAN J in-chao.Research on blind source separation based on kurtosis[J].Applied science and technology,2006,33(01):32.

备注/Memo

备注/Memo:
作者简介:席志红(1965-),女,教授,博士生导师,主要研究方向:信号检测与估计,E-mail:xizhihong@hrbeu.edu.cn.
更新日期/Last Update: 2010-03-16