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一种新的参数估计方法及其在混沌信号盲分离中的应用

王世元 冯久超

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一种新的参数估计方法及其在混沌信号盲分离中的应用

王世元, 冯久超

A novel method of estimating parameter and its application to blind separation of chaotic signals

Wang Shi-Yuan, Feng Jiu-Chao
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  • 为了有效地估计非线性映射中的参数,本文采用一种容积准则近似该映射的加权积分函数. 基于由状态空间模型建模的参数,提出了一种新的参数估计方法. 混沌信号的盲分离是一种具有挑战性的参数估计问题.将新的参数估计方法应用在该问题上, 实现混沌信号的有效重构.仿真结果表明该算法具有较快的收敛速度和较高的数值精度, 并能有效地分离原始混沌信号.
    To estimate effectively parameters of nonlinear mapping, a cubature rule is used to approximate the weighted integral of this mapping. In this paper, based on these parameters modeled by a state-space model, a novel parameter estimation is proposed. Blind separation of chaotic signals is a challenging problem. The proposed method is used to solve this problem to achieve the effective reconstruction of chaotic signals. Simulation results indicate that the proposed method has a faster convergence speed and a higher numerical accuracy, and can effectively separate original chaotic signals.
    • 基金项目: 国家自然科学基金(批准号: 61101232, 60872123, U0835001)和西南大学博士基金(批准号: SWU111027)资助的课题.
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 61101232, 60872123, U0835001), and the Doctoral Fund of Southwest University, China (Grant No. SWU111027).
    [1]

    Haykin S 2001 Kalman Filtering and Neural Networks (New York: John Wiley & Sons, Inc.) p128

    [2]

    Feng J C, Tse C K 2001 Phys. Rev. E 63 026202

    [3]

    Feng J C, Tse C K, Lau C M 2003 IEEE Trans. Circuits Syst. Part I 50 954

    [4]

    Wang S Y, Tian F C, Liu X, Wang J 2009 IEEE Signal Process. Lett. 16 275

    [5]

    Shi X Z 2008 Blind Signal Processing: Theory and Practice (Shanghai: Shanghai Jiaotong University Publishing house) p5 (in Chinese) [史习智 2008 盲信号处理:理论与实践(上海: 上海交通大学出版社) 第5页]

    [6]

    Wang B Y, Zheng W X 2006 IEEE Tran. Circuits Syst. Part Ⅱ 53 143

    [7]

    Chen Z, Zeng Y C, Fu Z J 2008 Acta Phys. Sin. 57 46 (in Chinese) [陈争, 曾以成, 付志坚 2008 物理学报 57 46]

    [8]

    Wang F P, Wang Z J, Guo J B 2002 Acta Phys. Sin. 51 474 (in Chinese) [汪芙平, 王赞基, 郭静波 2002 物理学报 51 474]

    [9]

    Li X X, Feng J C 2007 Acta Phys. Sin. 56 701 (in Chinese) [李雪霞, 冯久超 2007 物理学报 56 701]

    [10]

    Lv Q, Zhang X D, Jia Y 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing Pennsylvania, USA, March 18-23, 2005 p257

    [11]

    Hu Z H, Feng J C 2010 Journal of Southwest University (Nature Science Edition) 32 146 (in Chinese) [胡志辉, 冯久超 2010 西南大学学报(自然科学版) 32 146]

    [12]

    Merwe R V D, Wan E A 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing Utah, USA, May7-11, 2001 p3461

    [13]

    Arasaratnam I, Haykin S 2008 IEEE Tran. Signal Process 56 2589

    [14]

    Arasaratnam I, Haykin S 2009 IEEE Tran. Automatic Cont. 54 1254

    [15]

    Arasaratnam I, Haykin S 2011 Automatica 47 2245

    [16]

    Arasaratnam I, Haykin S, Elliott R J 2007 Proc. IEEE 95 953

    [17]

    Zhu X L, Zhang X D 2002 IEEE Signal Process Lett. 9 432

  • [1]

    Haykin S 2001 Kalman Filtering and Neural Networks (New York: John Wiley & Sons, Inc.) p128

    [2]

    Feng J C, Tse C K 2001 Phys. Rev. E 63 026202

    [3]

    Feng J C, Tse C K, Lau C M 2003 IEEE Trans. Circuits Syst. Part I 50 954

    [4]

    Wang S Y, Tian F C, Liu X, Wang J 2009 IEEE Signal Process. Lett. 16 275

    [5]

    Shi X Z 2008 Blind Signal Processing: Theory and Practice (Shanghai: Shanghai Jiaotong University Publishing house) p5 (in Chinese) [史习智 2008 盲信号处理:理论与实践(上海: 上海交通大学出版社) 第5页]

    [6]

    Wang B Y, Zheng W X 2006 IEEE Tran. Circuits Syst. Part Ⅱ 53 143

    [7]

    Chen Z, Zeng Y C, Fu Z J 2008 Acta Phys. Sin. 57 46 (in Chinese) [陈争, 曾以成, 付志坚 2008 物理学报 57 46]

    [8]

    Wang F P, Wang Z J, Guo J B 2002 Acta Phys. Sin. 51 474 (in Chinese) [汪芙平, 王赞基, 郭静波 2002 物理学报 51 474]

    [9]

    Li X X, Feng J C 2007 Acta Phys. Sin. 56 701 (in Chinese) [李雪霞, 冯久超 2007 物理学报 56 701]

    [10]

    Lv Q, Zhang X D, Jia Y 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing Pennsylvania, USA, March 18-23, 2005 p257

    [11]

    Hu Z H, Feng J C 2010 Journal of Southwest University (Nature Science Edition) 32 146 (in Chinese) [胡志辉, 冯久超 2010 西南大学学报(自然科学版) 32 146]

    [12]

    Merwe R V D, Wan E A 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing Utah, USA, May7-11, 2001 p3461

    [13]

    Arasaratnam I, Haykin S 2008 IEEE Tran. Signal Process 56 2589

    [14]

    Arasaratnam I, Haykin S 2009 IEEE Tran. Automatic Cont. 54 1254

    [15]

    Arasaratnam I, Haykin S 2011 Automatica 47 2245

    [16]

    Arasaratnam I, Haykin S, Elliott R J 2007 Proc. IEEE 95 953

    [17]

    Zhu X L, Zhang X D 2002 IEEE Signal Process Lett. 9 432

计量
  • 文章访问数:  2872
  • PDF下载量:  675
  • 被引次数: 0
出版历程
  • 收稿日期:  2011-12-14
  • 修回日期:  2012-02-26
  • 刊出日期:  2012-09-05

一种新的参数估计方法及其在混沌信号盲分离中的应用

  • 1. 西南大学电子信息工程学院, 重庆 400715;
  • 2. 华南理工大学电子与信息学院, 广州 510641
    基金项目: 

    国家自然科学基金(批准号: 61101232, 60872123, U0835001)和西南大学博士基金(批准号: SWU111027)资助的课题.

摘要: 为了有效地估计非线性映射中的参数,本文采用一种容积准则近似该映射的加权积分函数. 基于由状态空间模型建模的参数,提出了一种新的参数估计方法. 混沌信号的盲分离是一种具有挑战性的参数估计问题.将新的参数估计方法应用在该问题上, 实现混沌信号的有效重构.仿真结果表明该算法具有较快的收敛速度和较高的数值精度, 并能有效地分离原始混沌信号.

English Abstract

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