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

一种基于单位超球面平均的盲反卷积方法

Blind deconvolution method based on averaging over unit hypersphere
原文传递
导出
摘要 在机器学习理论中,为消除随机影响,对样本进行平均是一种广为使用的处理手段.为此,在具有相同物理激发过程的系统中,利用黎曼梯度的概念,构造了单位超球面上的平均算子.以地震数据盲反卷积为例,结合改进型的带状独立分量分析,提出了基于单位超球面平均的盲反卷积方法.仿真实验表明了所提出方法的有效性和优越性. Sample averaging is a widely used method in machine learning to rid the random influence.Therefore,in the system with the same physical process,an averaging operator on the unit hypersphere is constructed with the idea of Riemannian gradient.Combined with the improved banded independent component analysis in the problem of blind deconvolution for seismic data,a blind deconvolution method based on averaging over unit hypersphere is proposed. Simulation result shows the effectiveness and superiority of the method.
作者 程成 萧蕴诗 岳继光
机构地区 同济大学电子与信息工程学院
出处 《控制与决策》 EI CSCD 北大核心 2010年第5期758-762,共5页 Control and Decision
基金 国家自然科学基金项目(40872090)
关键词 带状独立分量分析 盲反卷积 单位超球面 黎曼梯度 Banded independent component analysis Blind deconvolution Unit hypersphere Riemannian gradient
分类号 TN911 [电子电信—通信与信息系统] O186 [理学—基础数学]
  • 相关文献

参考文献13

  • 1刘琚,何振亚.盲源分离和盲反卷积[J].电子学报,2002,30(4):570-576. 被引量：64
  • 2Kaplan S T, Ulrych T J. Blind deconvolution and ICA with a banded mixing matrix[C]. 4 th Int Symposium on ICA and Blind Signal Separation. Japan, 2003: 223-228.
  • 3Aws Al-Qaisi, Woo W L, Dlay S S. Blind seismic de.convolution of single channel using instantaneous independent component analysis[C]. 6th Int Symposium on Communication Systems Networks and Digital Signal Processing. Austria, 2008: 142-146.
  • 4Wiggings R A. Minimum entropy deconvolution[J]. Geoexploration, 1978, 16(1): 21-35.
  • 5Bellini S. Bussgang techniques for blind de, convolution and equalization[M]. New Jersey: Prentice Hall Englewood Cliffs, 1994: 8-52.
  • 6Haykin S. Adaptive Filtering[M]. New Jersey: Prentice Hall Englewood Cliffs, 1991.
  • 7Amari S, Fiori S. Geometrical methods in neural networks and learning[J]. Editorial for a Special Issue of Neurocomputing J, 2005, 67(8): 1-7.
  • 8Amari S. Natural gradient works efficiently in learning[J]. Neural Computation, 1998, 10(2): 251-276.
  • 9Fiori S, Tanaka T. An averaging method for a committee of special orthogonal group machines[C]. IEEE Int Symposium on Circuits and Systems. Washingdon, 2008: 2170-2173.
  • 10Hyvarinen A, Karhunen J, Oja E. Independent component analysis[M]. New York: Wiley, 2001.

二级参考文献42

  • 1凌燮亭.近场宽带信号源的盲分离[J].电子学报,1996,24(7):87-92. 被引量：5
  • 2Aapo Hyvarinen.Fast and Robust Fixed-Point Algorithms for Independent Component Analysis[J].IEEE Trans.on Neural Networks (S1045-9227).1999,10(3):626-634.
  • 3S Haykin.Unsupervised Adaptive Filtering,Vol.2:Blind Deconvolution[M].Wiley,2000.
  • 4A Nandi.Blind Estimation Using Higher-Order Statistics[M].Kluwer,1999.
  • 5S I Amari,A Cichocki.Adaptive blind signal processing-neural network approaches[J].Proceedings of the IEEE (S0018-9219).1998,86(10):2026-2048.
  • 6J Anemuller,B Kollmeier.Amplitude modulation decorrelation for convolutive blind source separation[C]//ICA2000.Helsinki,2000.215-220.
  • 7S I Amari.Natural gradient learning for over-and under-complete bases in ICA[J].Neural Computation (S0899-7667).1999,11(8):1875-1883.
  • 8Te-Won Lee,Michael S Lewicki,Mark Girolami,Terrence J Sejnowski.Blind Source Separation of More Sources Than Mixtures Using Overcomplete Representations[J].IEEE Signal Processing Letters (S1070-9908).1999,6(4):87-90.
  • 9Michael S Lewicki,Terrence J Sejnowski.Learning overcomplete representation[J].Neural Computation (S0899-7667).2000,12(2):337-365.
  • 10M Davies,N Mitianoudis.Simple mixture model for sparse overcomplete ICA[J].IEE Proc.-Vis.Image Signal Process.(S1350-245X).2004,151(1).

共引文献93

控制与决策
2010年 第5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