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多维信号非线性性的检验 被引量:1

Detecting nonlinearity in multidimensional signal
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摘要 在多变量时间序列中引入了广义冗余的概念,给出了广义冗余与广义关联积分之间的关系,通过广义冗余与线性冗余之间的差别获得了定性检验多维信号非线性性的方法。一个线性自回归模型和Lorenz系统的仿真计算证实了这种方法的有效性。 In the paper, we introduce the generalized redundancy of multivariate time series, get the relationship of generalized redundancy and generalized correlation integration. The distinction of generalized redundancy and linear redundancy can be used to test the nonlineartiy of multidimensional signals qualitatively. The method is tested by a linear autoregression model and the Lorenz system.
出处 《信号处理》 CSCD 2002年第1期20-23,共4页 Journal of Signal Processing
基金 国家自然科学基金资助项目(69874004)
关键词 多维信号 广义冗余 广义关联积分 非线性性 多变量时间序列 信号处理 校验 Multidimensional signals Generalized redundancy Generalized correlation integration Nonlinearity
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参考文献8

  • 1[1]H.D.I.Abarbanel,T.W. Frison,L.S.Tsimring,Obtaining order in a world of chaos, IEEE Signal Processing Magazine, 1998, 49-65.
  • 2[2]S.Haykin, J.Principe,Making sense of a complex world,IEEE Signal Processing Magazine, 1998, 66-81.
  • 3[3]J Theile S.Eubank,A.Longtin,et al,Testing for nonlinearity in time series:the method of surrogate data,Physica D, 58,1992, 77-94.
  • 4[4]C.J.Stam, J.P.M.Pijn, W.S.Pritchard, Reliable detection of nonlinearity in experimential time series with strong periodic components, Physica D, 112, 1998, 361-380.
  • 5[5]M.Palus, Testing for nonlinearity using redundancies:quantitative and qualitative aspects, Physica D 80, 1995,186-205.
  • 6[6]M.Palus, Detecting nonlinearity in multivariate time esries, Physics Letters A, 213, 1996, 138-147.
  • 7[7]D.Prichard,J. Thiler, Generalized redundancies for time series analysis, Physica D, 84, 1995,476-493.
  • 8[8]W.H.Press, B.P. Flannery, S.A.Teukolsky et al,Numerical recipes: the art of scientific computing, Cambridge University Press, Cambridge, M.A., 1986.

同被引文献7

  • 1H D I Abarbanel, T W Frison, L S Tsimring. Obtaining order in a word of chaos, IEEE Signal Processing Magazine, 1998,49-65.
  • 2S Haykin, J Principe, Making sense of a complex world,IEEE Signal Processing Magazine, 1998,66-81.
  • 3J Theiler, S Eubank, A Longtin et al. Testing for nonlinearity in lime series: the method of surrogate data,Physica D,58,1992,77 -94.
  • 4C J Stam, J P M Pijn, W S Pritchard. Reliable detection of nonlinearity in experimental time series with strong period components, Physica D, 112,1998,361-380.
  • 5M Palus.Testing of nonlinearity using redundancies:quantitative and qualitative aspects, Physica D, 80, 1995,186-205.
  • 6M Palus. Detecting nonlinearity in multivariate time series, Physics Letters A, 213, 1996, 138-147.
  • 7D Prichard, J Theiler. Generating surrogate data for time series with several simultaneously measured variables,Physical Review Letters,73(7),1994,951-954.

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