基于FastICA的工频干扰消除算法
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摘要
阐述了独立成分分析(Independent Components Analysis,ICA)的基本原理,将快速ICA(FastICA)算法应用于消除地震信号中的工频干扰,对输出信号的相关系数绝对值进行对比.结果表明:与传统的工频干扰消除技术相比,FastICA算法可以更加有效地消除微信号中的工频干扰.
The fundamentals of independent component analysis(ICA)is elaborated.The FastICA algorithm is applied to eliminating the power interference of seismic signal,the absolute values of correlation coefficient of the output signals are compared.The simulation results show that compared with the traditional power interference eliminating techniques,the FastICA algorithm can be more effective in removing the power interference of micro-signals.
The fundamentals of independent component analysis(ICA)is elaborated.The FastICA algorithm is applied to eliminating the power interference of seismic signal,the absolute values of correlation coefficient of the output signals are compared.The simulation results show that compared with the traditional power interference eliminating techniques,the FastICA algorithm can be more effective in removing the power interference of micro-signals.
引文
[1]NADAL J,PARGA N.Nonlinear neurons in low-noise limit:Afactorial code maximizes information transfer[J].Network,1994,5(3):565—581.
[2]BELL A,SEJNOWSKI T.An information maximization approach toblind separation and blind de-convolution[J].Neural Computation,1995,7(6):1129—1159.
[3]HYV魧RINEN A,OJA E.Independent component analysis:Algo-rithms and applications[J].Neural Networks,2000,13(4/5):411—430.
[4]HYV魧RINEN A,PAJUNEN P.Nonlinear independent componentanalysis:Existence and uniqueness results[J].Neural Networks,1999,12(3):429—439.
[5]JUTTEN C,ZADEH M,HOSSEINI S.Three easy ways for separat-ing nonlinear mixtures[J].Signal Processing,2004,84(2):217—229.
[6]LIU W,RAJAPAKSE J.Approach and applications of constrainedICA[J].Trans Neural Networks,2005,16(1):203—212.
[7]PLUMBLEY M.Algorithms for nonnegative component independentanalysis[J].Trans Neural Networks,2003,14(3):534—543.
[8]王世海,陈向东,毕雪,等.多分辨率子带分解的独立分量分析算法在红外图像去噪上的应用[J].电子技术应用,2007(6):66—68.
[9]LEE T,LEWICKI M,GIROLAMI M,et al.Blind source separationof more sources than mixtures using over-complete representations[J].Signal Processing Letters,1999,4(5):205—208.
[10]白琳,陈豪.一种奇异混合阵的盲信号提取算法[J].空间电子技术,2012(1):6—10,22.
[11]李远清,张丽清,刘永清.一种奇异混合信号盲分离的神经网络模型[J].控制理论与应用,2001,18(1):105—108.
[12]肖俊,何为伟.一种基于信息最大化的时延混合盲分离算法[J].井冈山师范学院学报:自然科学版,2005,26(3):12—14.
[13]HYV魧RINEN A,KARHUNEN J,OJA C.Independent ComponentAnalysis[M].New York:Wiley,2001:293—304.
[14]杨福生,洪波,唐庆玉.独立分量分析及其在生物医学工程中的应用[J].国外医学:生物医学工程分册,2000,23(3):129—134.
[15]郭松.独立分量分析及其应用研究[D].武汉:武汉大学,2004:13—20.
[16]刘喜武,刘洪,李幼铭.独立分量分析及其在地震信息处理中的应用初探[J].地球物理学进展,2003,18(1):90—96.
[17]吴小培,詹长安,周荷琴,等.采用独立分量分析的方法消除信号中的工频干扰[J].中国科技大学学报,2000,30(6):671—676.
[2]BELL A,SEJNOWSKI T.An information maximization approach toblind separation and blind de-convolution[J].Neural Computation,1995,7(6):1129—1159.
[3]HYV魧RINEN A,OJA E.Independent component analysis:Algo-rithms and applications[J].Neural Networks,2000,13(4/5):411—430.
[4]HYV魧RINEN A,PAJUNEN P.Nonlinear independent componentanalysis:Existence and uniqueness results[J].Neural Networks,1999,12(3):429—439.
[5]JUTTEN C,ZADEH M,HOSSEINI S.Three easy ways for separat-ing nonlinear mixtures[J].Signal Processing,2004,84(2):217—229.
[6]LIU W,RAJAPAKSE J.Approach and applications of constrainedICA[J].Trans Neural Networks,2005,16(1):203—212.
[7]PLUMBLEY M.Algorithms for nonnegative component independentanalysis[J].Trans Neural Networks,2003,14(3):534—543.
[8]王世海,陈向东,毕雪,等.多分辨率子带分解的独立分量分析算法在红外图像去噪上的应用[J].电子技术应用,2007(6):66—68.
[9]LEE T,LEWICKI M,GIROLAMI M,et al.Blind source separationof more sources than mixtures using over-complete representations[J].Signal Processing Letters,1999,4(5):205—208.
[10]白琳,陈豪.一种奇异混合阵的盲信号提取算法[J].空间电子技术,2012(1):6—10,22.
[11]李远清,张丽清,刘永清.一种奇异混合信号盲分离的神经网络模型[J].控制理论与应用,2001,18(1):105—108.
[12]肖俊,何为伟.一种基于信息最大化的时延混合盲分离算法[J].井冈山师范学院学报:自然科学版,2005,26(3):12—14.
[13]HYV魧RINEN A,KARHUNEN J,OJA C.Independent ComponentAnalysis[M].New York:Wiley,2001:293—304.
[14]杨福生,洪波,唐庆玉.独立分量分析及其在生物医学工程中的应用[J].国外医学:生物医学工程分册,2000,23(3):129—134.
[15]郭松.独立分量分析及其应用研究[D].武汉:武汉大学,2004:13—20.
[16]刘喜武,刘洪,李幼铭.独立分量分析及其在地震信息处理中的应用初探[J].地球物理学进展,2003,18(1):90—96.
[17]吴小培,詹长安,周荷琴,等.采用独立分量分析的方法消除信号中的工频干扰[J].中国科技大学学报,2000,30(6):671—676.
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