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由单变量受扰观测序列估计非线性系统重影轨迹

张政伟 樊养余 王结太

张政伟, 樊养余, 王结太. 由单变量受扰观测序列估计非线性系统重影轨迹[J]. 电子与信息学报, 2008, 30(2): 371-374. doi: 10.3724/SP.J.1146.2007.00396
引用本文: 张政伟, 樊养余, 王结太. 由单变量受扰观测序列估计非线性系统重影轨迹[J]. 电子与信息学报, 2008, 30(2): 371-374. doi: 10.3724/SP.J.1146.2007.00396
Zhang Zheng-wei, Fan Yang-yu, Wang Jie-tai. Estimation of Shadowing Trajectory of the Nonlinear Systemfrom a Noisy Scalar Series[J]. Journal of Electronics & Information Technology, 2008, 30(2): 371-374. doi: 10.3724/SP.J.1146.2007.00396
Citation: Zhang Zheng-wei, Fan Yang-yu, Wang Jie-tai. Estimation of Shadowing Trajectory of the Nonlinear Systemfrom a Noisy Scalar Series[J]. Journal of Electronics & Information Technology, 2008, 30(2): 371-374. doi: 10.3724/SP.J.1146.2007.00396

由单变量受扰观测序列估计非线性系统重影轨迹

doi: 10.3724/SP.J.1146.2007.00396
基金项目: 

陕西省自然科学基金(2003F40)资助课题

Estimation of Shadowing Trajectory of the Nonlinear Systemfrom a Noisy Scalar Series

  • 摘要: 同宿切面和同宿截面的存在使得非双曲线型非线性系统重影轨迹的估计变得十分困难。该文在充分挖掘非线性系统本身特性的基础上,提出了一种估计非双曲线型非线性系统重影轨迹的新方法。不同于以往算法,该方法首先计算受扰序列的局部稳定流和不稳定流方向,进而确定同宿切面存在的位置,很大程度上降低了同宿切面对算法性能的影响,并可精确确定重影轨迹的长度;也不同于现有文献忽视同宿截面对算法性能影响的做法,该文研究得出同宿截面点间的最小距离和干扰噪声幅度二者间的关系,首次定量地估计了同宿截面点可能对算法造成的影响,这无疑对其它算法也将是一个有益的启示。
  • Brian A C, Hseyin K, and Kenneth J P. Shadowing orbits ofordinary differential equations [J].Journal of Computationaland Applied Mathematics.1994, 52(1-3):35-43[2]Eric J K and Thomas S. Noise reduction in chaotictime-series data : A survey of common methods [J].Phys. Rev.E.1993, 48(3):1752-1763[3]Grebogi C, Hammel S M, and Yorke A J. Do numerical orbitsof chaotic dynamical process represent true orbits [J]. JComplexity, 1987, 3(2): 136-145.[4]Bowen R. -limit sets for axiom A diffeomorphisms [J].JDiff. Eqs.1975, 18(2):333-339[5]Grebogi C, Hammel S M, Yorke J A, and Sauer T. Shadowingof physical trajectories in chaotic dynamics: containment andrefinement [J].Phys. Rev. Lett.1990, 65(13):1527-1530[6]Sauer T and Yorke J A. Rigorous verification of trajectoriesfor the computer simulation of dynamical systems [J].Nonlinearity.1991, 4(3):961-979[7]Farmer J D and Sidorowich J J. Optimal shadowing and noisereduction [J].Physica D.1991, 47(3):373-392[8]Walker D M and Mees A I. Noise reduction of chaotic systemsby Kalman filtering and by shadowing [J].Int. J. Bifurc.Chaos.1997, 7(3):769-779[9]Davies M. Noise reduction by gradient descent [J]. Int. J.Bifurc. Chaos, 1992, 3(1): 113-118.[10]David R and Kevin J. Convergence properties of gradientdescent noise reduction [J].Physical D.2002, 165(1-2):26-47[11]Grassberger P, Hegger R, Kantz H, Schaffrath C, andSchreiber T. On noise reduction methods for chaotic data [J].Chaos.1993, 3(2):127-141[12]Takens F. Detecting Strange Attractors In Fluid Turbulence.in : Dynamical Systems and Turbulence [M]. edited by RandD and Young L S, Berlin, Springer, 1981: 365-381.[13]Voss H U, Timmer J, and Kurths J. Nonlinear dynamicaldystem identification from uncertain and indirectmeasurements [J].Int. J. Bifurc. Chaos.2004, 14(6):1905-1933[14]Oseledec V I. A multiplicative ergodic theorem : Lyapunovcharacteristic numbers for dynamical systems [J]. Trans. onMoscow Math. Soc., 1968, 19: 197-221.
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出版历程
  • 收稿日期:  2007-03-20
  • 修回日期:  2007-09-26
  • 刊出日期:  2008-02-19

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