期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于批处理和核函数的非线性盲源分离算法 被引量：1

Nonlinear Blind Source Separation Algorithm Based on Batch and Kernel Function
下载PDF
导出
摘要 针对基于核函数的非线性盲源分离算法性能对核函数及其参数选择依赖性强这一问题,提出采用批处理方法代替聚类和核主成分分析方法来构造低维近似子空间的正交基,以改进基于核函数的非线性盲源分离算法对核函数及其参数变化的稳健性,并对这种改进的非线性盲源分离算法进行了完整的分析。通过仿真实验,对分离信号与源信号求相似度,可以看到提出的基于批处理的非线性盲源分离算法能够取得更稳健、准确的分离效果。 To solve the problem that the performance of nonlinear blind source separation algorithm based on kernel function is dependent on the kernel function and its parameters, this paper proposes using batch methods to construct orthonormal basis for reduced dimension approximate subspace instead of clustering and KPCA metheds. This improved nonlinear blind source separation algorithm based on batch and kernel feature space is investigated firstly, and then is used to improve the robustness to the variety of the kernel function and its parameters. The simulation results illustrate that the algorithm based on batch is more robust and is relatively simple and effective.
作者 禹华钢 高俊 黄高明
机构地区 海军工程大学电子工程学院
出处 《电讯技术》 北大核心 2011年第10期35-40,共6页 Telecommunication Engineering
基金 国家高技术研究发展计划(863计划)项目(2010AA7010422)~~
关键词 信号处理 非线性盲源分离 核函数 聚类 批处理 相似度 signal processing nonlinear blind source separation kemel function clustering batch resemble degree
分类号 TN911.7 [电子电信—通信与信息系统]
  • 相关文献

参考文献14

  • 1HyvarinenA, Karhunen J, Oja E. Independent Component Analysis[M]. New York: Wiley, 2001.
  • 2高鹰,姚振坚,李朝晖,谢胜利.基于广义特征值和核函数的非线性盲分离算法[J].系统工程与电子技术,2006,28(10):1490-1493. 被引量：5
  • 3Ziehe A, Mttller K R. TDSEP- an efficient algorithm for blind separation using time structure [C]//Proceedings of 1998 International Conference on Artifical Neural Networks. Springer, Berlin: [ s. n. ], 1998 : 675 - 680.
  • 4Bach F, Jordan M. Kernel independent component analysis [J]. Journal of Machine Learning Research,2002(3) :1 - 48.
  • 5Lee T W, Koehler B U, Orglmeister R. Blind source separation of nonlinear mixing models[ C]//Proceedings of 1997 IEEE International Workshop on Neural Networks for Signal Processing. Amelia Island, FL, USA: IEEE, 1997 : 406 - 415.
  • 6Woo W L, Knor L C. Blind restoration of nonlinearly mixed signals Ring mtdtiplayer polynomial neural network[ J ]. IEE Proceedings of Vision Image and Signal Processing,2004,151 (1):51-61.
  • 7Valpola H, Giannakopoulos X, Honkela A, et al. Nonlinear independent component analysis using ensemble learning: experiments and discussion [ C ]//Proceedings of International Workshop on Independent Component Analysis and Blind Signal Separation. Helsinki, Finland: IEEE, 2000: 351 - 356.
  • 8Valpola H, Oja E, Ilin A, et al. Nonlinear blind source separation by variational Bayesian learning[ J]. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2003 (3) : 532 - 541.
  • 9Ziehe A, Kawanabe M, Harmeling S. Blind separation of post - nonlinear mixtures using linearizing transformations and temporal decorrelation[J]. Journal of Machine Learning Research, 2003(4) : 1319- 1338.
  • 10AlmeidaL B. MISEP- linear and nonlinear ICA based on mutual information [ J ]. Journal of Machine Learning Research, 2003(4): 1297- 1318.

二级参考文献12

  • 1Pajunen P,Hyvarinen A,Karhunen J.Nonlinear blind source separation by self-organizing maps[C]//Proc.of Int.Conf.on Neural Information Processing,1996:1207-1210.
  • 2Pajunen P,Karhunen J.A maximum likelihood approach to non-linear blind source separation[C]// Proc.of the Int.Conf.on Artificial Neural Networks (ICANN'97),Lausanne,Switzerland,1997:541-546.
  • 3Tan Y,Wang J.Nonlinear blind source separation using a radial basis function network.[J].IEEE Trans.on Neural Networks,2001,12(1):124-134.
  • 4Tan Y,Wang J.Nonlinear blind source separation using higher order statistical and a genetic algorithm[J].IEEE Trans.on Evolutionary Computation,2001,5(6):600-612.
  • 5Woo W L,Knor L C.Blind restoration of nonlinearly mixed signals using multiplayer polynomial neural network[J].IEE Proc.-Vis.Image Signal Process.,2004,151(1).
  • 6Lee T W,Koehler B U,Orglmeister R.Blind source separation of nonlinear mixing models[C]/ / IEEE International Workshop on Neural Networks for Signal Processing,1997:406-415.
  • 7Valpola H,Giannakopoulos X,Honkela A,et al.Nonlinear independent component analysis using ensemble learning:experiments and discussion[C]//Proc.of Int.Workshop on Independent Component Analysis and Blind Signal Separation (ICA2000),Helsinki,Finland,2000:351-356.
  • 8Valpola H,Oja E,Ilin A,et al.Nonlinear blind source separation by variational Bayesian learning[C]// IEICE Trans Fundamentals of Electronics,Communications and Computer Sciences,2003,E86-A(3):532-541.
  • 9Ziehe A,Kawanabe M,Harmeling S.Blind separation of postnonlinear mixtures using linearizing transformations and temporal decorrelation[J].Journal of Machine Learning Research,2003 (4):1319-1338.
  • 10Scholkopf B,Smola A J,Muller K R.Nonlinear component analysis as a kernel eigenvalue problem[J].Neural Computation,1998,10(5):1299-1319.

共引文献4

同被引文献15

  • 1Diamantaras K,Papadimitriou T,Vranou G. Blind separa-tion of multiple binary sources from one nonlinear mixture[C] / / Proceedings of 2011 IEEE International Conferenceon Acoustics, Speech and Signal Processing. Prague:IEEE,2011:2108-2111.
  • 2Almeida M,Almeida L. Nonlinear separation of show throughimage mixtures using a physical model trained with ICA[J].Signal Processing,2012,29(4):872-884.
  • 3Almeida L B. Linear and nonlinear ICA based on mutualinformation-the MISEP method[J]. Signal Processing,2004,84(2):231-245.
  • 4Sun Z L. An extension of MISEP for post-nonlinear-line-ar mixture separation [J]. IEEE Transactions on CircuitsSystem,2009,56(8):654 -658.
  • 5Duarte L T,Jutten C. A mutual information minimizationapproach for a class of nonlinear recurrent separating sys-tems[C] / / Proceedings of 2007 IEEE Workshop on Ma-chine Learning for Signal Processing. Thessaloniki:IEEE,2007:122 -127.
  • 6Chen Wei,Li C K,Wai L W,et al. Post-nonlinear underde-termined ICA by Bayesian statistics[C] / / Proceedings ofICA 2006. Charleston,SC,USA:IEEE,2006:773-780.
  • 7Duarte L T,Jutten C,Moussaoui S. A Bayesian NonlinearSource Separation Method for Smart Ion-Selective ElectrodeArrays [J]. Sensors Journal,2009,9(12):1763-1771.
  • 8Ren D X,Mao Y S. Extracting post-nonlinear signal withreference[J]. Computers and Electrical Engineering,2011,37(6):1171-1181.
  • 9Ren D X,Mao Y S. Extracting post-nonlinear signal withspecific kurtosis range [J]. Applied Mathematics andComputation,2012,218(9):5726-5738.
  • 10Hameling S,Ziehe A,Kawanabe A. Kernel based nonlin-ear blind source separation [J]. Neural Computation,2013(15):1089-1124.

引证文献1

电讯技术
2011年 第10期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部