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基于Weiner模型超混沌l系统的自适应辨识

赵益波 张秀再 孙心宇

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基于Weiner模型超混沌l系统的自适应辨识

赵益波, 张秀再, 孙心宇

Adaptive identification for hyperchaotic l system based on Weiner model

Zhao Yi-Bo, Zhang Xiu-Zai, Sun Xin-Yu
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  • 为了能实时而有效地辨识参数不确定的超混沌l系统,以便于对该系统进行控制或跟踪,本文提出了一种基于Wiener模型自适应分段线性(PWL)滤波器的超混沌系统辨识方法. Wiener模型的线性部分采用了线性横向滤波器,非线性部分用分段线性滤波器近似表示. 根据最小均方误差准则导出了滤波器参数更新算法,并进一步推导出算法的收敛性条件. 计算机仿真证实了该自适应滤波器辨识超混沌系统的有效性. 该方法不仅克服了自适应线性滤波器难以辨识出这类强非线性系统,而且比其他非线性自适应滤波器的计算复杂性低得多.
    In order to be able to identify the hyper-chaotic l system with uncertain parameters effectively in real time, so that hyper-chaotic system control and synchronization tracking can be applied, this paper presents a system identification method for the hyper-chaotic system based on Wiener model. The linear part of the Wiener model consists of linear transversal filters, while the nonlinear part is represented approximately by piecewise linear filters. According to the minimum mean square error criterion, the filter parameter updated algorithm is derived, and the convergence condition is also obtained. Simulation results confirm the effectiveness of the adaptive filter for the identification of hyper-chaotic systems. The presented method not only overcomes the difficulty to identify a strongly nonlinear system only by adaptive linear filters, but also have a lower computational complexity compared with other non-linear adaptive filters.
    • 基金项目: 江苏高校优势学科建设工程资助项目、南京信息工程大学科研基金(批准号:20110439)、优秀博士论文作者专项资金(批准号:27122)和国家自然科学基金(批准号:51077057)资助的课题.
    • Funds: Project in part supported by a Fund by the Priority Academic Program Development of Jiangsu Higher Education Institutions, Nanjing University of Information Science Nanjing University of Information Science Technology Research Foundation (Grant No. 20110439), the Outstanding Doctoral Dissertation Project of Special Funds (Grant No. 27122), and the National Natural Science Foundation of China, (Grant No. 51077057).
    [1]

    Wills A, Schn T B, Ljung L, Ninness B 2013 Automatica 49 70

    [2]

    Shafiee G, Arefi M M, Jahed-Motlagh M R, Jalali A A 2008 Chem. Eng. J. 143 282

    [3]
    [4]

    Peng J Z, Dubay R 2011 ISA Trans 50 588

    [5]
    [6]

    Silvina I B, Jos L F 2011 Comput. Chem. Eng. 35 2867

    [7]
    [8]

    Zhao Z J, Zheng X H, Shen L 2010 J. Circ. Syst. 15 11 (in Chinese) [赵知劲, 郑晓华, 沈雷 2010 电路与系统学报 15 11]

    [9]
    [10]

    Figueroa J L, Cousseau J E, Figueiredo R J P de 2004 Circ. Syst. Signal. Proc. 23 365

    [11]
    [12]

    Liu X F, Yang X Q, Zheng N N 2012 Neurocomputing 79 132

    [13]
    [14]

    Ma T D, Jiang W B, Fu J, Chai Y 2012 Acta Phys. Sin. 61 160506 (in Chinese)[马铁东, 江伟波, 浮洁, 柴毅 2012 物理学报 61 160506]

    [15]
    [16]

    Huang L L, Qi X 2013 Acta Phys. Sin. 62 080507 (in Chinese)[黄丽莲, 齐雪 2013 物理学报 62 080507]

    [17]
    [18]

    Yang J, Sun Q Y, Yang D S 2012 Acta Phys. Sin. 61 200511 (in Chinese)[杨珺, 孙秋野, 杨东升 2012 物理学报 61 200511]

    [19]
    [20]
    [21]

    Yang D S, Liu Z W, Zhao Y, Liu Z B 2012 Chin. Phys. B 21 040503

    [22]
    [23]

    Xu Y H, Li B, Zhou W N, Fang J A 2012 Nonlinear. Dynam. 70 289

    [24]
    [25]

    Luo R Z, Wang Y L 2012 Chaos. 22 023109

    [26]
    [27]

    Li D, Deng L M, Du Y X, Yang Y 2012 Acta Phys. Sin. 61 050502 (in Chinese)[李东, 邓良明, 杜永霞, 杨媛 2012 物理学报 61 050502]

    [28]
    [29]

    Zhang H L, Song L L 2013 Acta Phys. Sin. 62 190508 (in Chinese)[张宏立, 宋莉莉 2013 物理学报 62 190508]

    [30]
    [31]

    Zhu D R, Liu C X, Yan B N 2012 Chin. Phys. B 21 090509

    [32]

    Gu W D, Sun Z Y, Wu X M, Yu C B 2013 Chin. Phys. B 22 090203

    [33]
    [34]

    Haykin S 2002 Adaptive filter theory (New York: Pearson Education)

    [35]
    [36]

    Zheng C D, Shan Q H, Zhang H G, Wang Z S 2013 IEEE Trans. Neural. Networks. Learning. Syst. 24 800

    [37]
    [38]
    [39]

    Chen A M, Junan Lu J N, L J H, Yu S M 2006 Phys. A 364 103

    [40]

    Chua L O, Deng A C 1988 IEEE Trans. Circ. Syst. 35 101

    [41]
    [42]
    [43]

    Julian P 1999 High level canonical piecewise linear representation: Theory and applications. (Ph. D. thesis in Systems Control, Universidad Nacional del Sur, UMI Dissertation Services)

  • [1]

    Wills A, Schn T B, Ljung L, Ninness B 2013 Automatica 49 70

    [2]

    Shafiee G, Arefi M M, Jahed-Motlagh M R, Jalali A A 2008 Chem. Eng. J. 143 282

    [3]
    [4]

    Peng J Z, Dubay R 2011 ISA Trans 50 588

    [5]
    [6]

    Silvina I B, Jos L F 2011 Comput. Chem. Eng. 35 2867

    [7]
    [8]

    Zhao Z J, Zheng X H, Shen L 2010 J. Circ. Syst. 15 11 (in Chinese) [赵知劲, 郑晓华, 沈雷 2010 电路与系统学报 15 11]

    [9]
    [10]

    Figueroa J L, Cousseau J E, Figueiredo R J P de 2004 Circ. Syst. Signal. Proc. 23 365

    [11]
    [12]

    Liu X F, Yang X Q, Zheng N N 2012 Neurocomputing 79 132

    [13]
    [14]

    Ma T D, Jiang W B, Fu J, Chai Y 2012 Acta Phys. Sin. 61 160506 (in Chinese)[马铁东, 江伟波, 浮洁, 柴毅 2012 物理学报 61 160506]

    [15]
    [16]

    Huang L L, Qi X 2013 Acta Phys. Sin. 62 080507 (in Chinese)[黄丽莲, 齐雪 2013 物理学报 62 080507]

    [17]
    [18]

    Yang J, Sun Q Y, Yang D S 2012 Acta Phys. Sin. 61 200511 (in Chinese)[杨珺, 孙秋野, 杨东升 2012 物理学报 61 200511]

    [19]
    [20]
    [21]

    Yang D S, Liu Z W, Zhao Y, Liu Z B 2012 Chin. Phys. B 21 040503

    [22]
    [23]

    Xu Y H, Li B, Zhou W N, Fang J A 2012 Nonlinear. Dynam. 70 289

    [24]
    [25]

    Luo R Z, Wang Y L 2012 Chaos. 22 023109

    [26]
    [27]

    Li D, Deng L M, Du Y X, Yang Y 2012 Acta Phys. Sin. 61 050502 (in Chinese)[李东, 邓良明, 杜永霞, 杨媛 2012 物理学报 61 050502]

    [28]
    [29]

    Zhang H L, Song L L 2013 Acta Phys. Sin. 62 190508 (in Chinese)[张宏立, 宋莉莉 2013 物理学报 62 190508]

    [30]
    [31]

    Zhu D R, Liu C X, Yan B N 2012 Chin. Phys. B 21 090509

    [32]

    Gu W D, Sun Z Y, Wu X M, Yu C B 2013 Chin. Phys. B 22 090203

    [33]
    [34]

    Haykin S 2002 Adaptive filter theory (New York: Pearson Education)

    [35]
    [36]

    Zheng C D, Shan Q H, Zhang H G, Wang Z S 2013 IEEE Trans. Neural. Networks. Learning. Syst. 24 800

    [37]
    [38]
    [39]

    Chen A M, Junan Lu J N, L J H, Yu S M 2006 Phys. A 364 103

    [40]

    Chua L O, Deng A C 1988 IEEE Trans. Circ. Syst. 35 101

    [41]
    [42]
    [43]

    Julian P 1999 High level canonical piecewise linear representation: Theory and applications. (Ph. D. thesis in Systems Control, Universidad Nacional del Sur, UMI Dissertation Services)

计量
  • 文章访问数:  1824
  • PDF下载量:  486
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-10-14
  • 修回日期:  2014-03-13
  • 刊出日期:  2014-07-05

基于Weiner模型超混沌l系统的自适应辨识

  • 1. 南京信息工程大学, 江苏省气象探测与信息处理重点实验室, 南京 210044
    基金项目: 

    江苏高校优势学科建设工程资助项目、南京信息工程大学科研基金(批准号:20110439)、优秀博士论文作者专项资金(批准号:27122)和国家自然科学基金(批准号:51077057)资助的课题.

摘要: 为了能实时而有效地辨识参数不确定的超混沌l系统,以便于对该系统进行控制或跟踪,本文提出了一种基于Wiener模型自适应分段线性(PWL)滤波器的超混沌系统辨识方法. Wiener模型的线性部分采用了线性横向滤波器,非线性部分用分段线性滤波器近似表示. 根据最小均方误差准则导出了滤波器参数更新算法,并进一步推导出算法的收敛性条件. 计算机仿真证实了该自适应滤波器辨识超混沌系统的有效性. 该方法不仅克服了自适应线性滤波器难以辨识出这类强非线性系统,而且比其他非线性自适应滤波器的计算复杂性低得多.

English Abstract

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