独立分量分析方法及其在地理与环境科学中的应用
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摘要
综述了独立分量分析(ICA)问题的模型、原理,以及定义在基本ICA问题上的各种算法,包括信息最大化in-fom ax算法、负熵最大化法、最大似然法等,并对各算法性能作了比较。并介绍了可获得的ICA算法系统fastica和oolabss及其运行平台,说明了ICA在地理与环境问题中的应用。
This paper summarizes the principle and the model of Independent Component Analysis,and various algorithms based on the basic ICA problem,including the HJ neural network,the infomax and maximum negentropy algorithm.The performances of the these methods are compared,the gainable system of ICA algorithm on the web is introduced,the applications of ICA in the Geographical and Environmental fields are discribed and explained.
This paper summarizes the principle and the model of Independent Component Analysis,and various algorithms based on the basic ICA problem,including the HJ neural network,the infomax and maximum negentropy algorithm.The performances of the these methods are compared,the gainable system of ICA algorithm on the web is introduced,the applications of ICA in the Geographical and Environmental fields are discribed and explained.
引文
[1]HYVAR INEN A,OJA E.Independent ComponentAnalysis:A lgoritbm s and App lication.[J].Neural Networks,2000(13):410-430.
[2]彭煊,刘金福,王炳锡.基于独立分量分析的语音增强[J].信号处理,2002,18(5):477-479.
[3]曾生根,夏德深.独立分量分析在多光谱遥感图像分类中的应用[J].计算机工程与应用,2004,21:108-110,145.
[4]曾生根,朱宁波,包晔,等.一种改进的快速独立分量分析算法及其在图象分离中的应用[J].中国图象图形学报,2003,8(10):1159-1165.
[5]杨行峻,郑君里.人工神经网络与盲信号处理[M].北京:清华大学出版社,2003.
[6]CAO X R,LIU R W.General Approach to B lind SourceSeparation.IEEE Trans[J].S ignal Processing,1996,78:753-766.
[7]张旭秀,邱天爽.独立分量分析原理及其应用[J].大连铁道学院学报,2003,24(2):64-68.
[8]吴小培,冯焕清.基于独立分量分析的图象分离技术及应用[J].中国图象图形学报,2001,6(2):133-137.
[9]HYVAR INEN A.Independent Component Analysis:A Tu-torial[EB/OL].http://www.c is.hut.fi/aapo/papers/IJC-NN99 tutorialweb/node15.htm l,2005-05-02.
[10]CARDPSP J F.Inform ax and M aximum L ikelihood forB lind Source Separation[J].IEEE S ignal Proces-sing let-ters,1997,4:112-114.
[11]刘喜武,刘洪,李幼铭.独立分量分析及其在地震信息处理中应用初探[J].地球物理学进展,2003,18(1):90-96.
[12]刘喜武,刘洪.地震盲反褶积综述[J].地球物理学进展,2003,18(2):203-209.
[13]何振亚,刘琚,杨绿溪,等.盲均衡和信道参数估计的一种ICA和进化计算方法[J].中国科学(E辑),2000,30(2):142-149.
[14]杨士兴,应用独立成分分析方法分离集水区河川污染源之研究[EB/OL].http://www.geog.ntu.edu.tw/m ain/paper/d81208004,2005-05-02.
[15]HYVAR INEN A.PAJUNER P.Non linear IndependentComponent Analysis:Existence and Un iqueness Resu lts[J].S ignal Processing,1998,64:301-313.
[16]HYVAR INEN A.Independent ComponentAnalysis in thePresence of Gaussian Noise by M axim izing Joint L ikeli-hood[J].Neuro Computing,1998,22:49-67.
[17]王开发,余小平.具有周期和时滞的Hopfield型连续神经网络的周期解[J].重庆师范学院学报(自然科学版),1998,15(4):24-26.
[2]彭煊,刘金福,王炳锡.基于独立分量分析的语音增强[J].信号处理,2002,18(5):477-479.
[3]曾生根,夏德深.独立分量分析在多光谱遥感图像分类中的应用[J].计算机工程与应用,2004,21:108-110,145.
[4]曾生根,朱宁波,包晔,等.一种改进的快速独立分量分析算法及其在图象分离中的应用[J].中国图象图形学报,2003,8(10):1159-1165.
[5]杨行峻,郑君里.人工神经网络与盲信号处理[M].北京:清华大学出版社,2003.
[6]CAO X R,LIU R W.General Approach to B lind SourceSeparation.IEEE Trans[J].S ignal Processing,1996,78:753-766.
[7]张旭秀,邱天爽.独立分量分析原理及其应用[J].大连铁道学院学报,2003,24(2):64-68.
[8]吴小培,冯焕清.基于独立分量分析的图象分离技术及应用[J].中国图象图形学报,2001,6(2):133-137.
[9]HYVAR INEN A.Independent Component Analysis:A Tu-torial[EB/OL].http://www.c is.hut.fi/aapo/papers/IJC-NN99 tutorialweb/node15.htm l,2005-05-02.
[10]CARDPSP J F.Inform ax and M aximum L ikelihood forB lind Source Separation[J].IEEE S ignal Proces-sing let-ters,1997,4:112-114.
[11]刘喜武,刘洪,李幼铭.独立分量分析及其在地震信息处理中应用初探[J].地球物理学进展,2003,18(1):90-96.
[12]刘喜武,刘洪.地震盲反褶积综述[J].地球物理学进展,2003,18(2):203-209.
[13]何振亚,刘琚,杨绿溪,等.盲均衡和信道参数估计的一种ICA和进化计算方法[J].中国科学(E辑),2000,30(2):142-149.
[14]杨士兴,应用独立成分分析方法分离集水区河川污染源之研究[EB/OL].http://www.geog.ntu.edu.tw/m ain/paper/d81208004,2005-05-02.
[15]HYVAR INEN A.PAJUNER P.Non linear IndependentComponent Analysis:Existence and Un iqueness Resu lts[J].S ignal Processing,1998,64:301-313.
[16]HYVAR INEN A.Independent ComponentAnalysis in thePresence of Gaussian Noise by M axim izing Joint L ikeli-hood[J].Neuro Computing,1998,22:49-67.
[17]王开发,余小平.具有周期和时滞的Hopfield型连续神经网络的周期解[J].重庆师范学院学报(自然科学版),1998,15(4):24-26.
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