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基于反演自适应动态滑模的FitzHugh-Nagumo神经元混沌同步控制

于海涛 王江

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基于反演自适应动态滑模的FitzHugh-Nagumo神经元混沌同步控制

于海涛, 王江

Chaos synchronization of FitzHugh-Nagumo neurons via backstepping and adaptive dynamical sliding mode control

Yu Hai-Tao, Wang Jiang
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  • 本文采用反演自适应动态滑模控制实现耦合FitzHugh-Nagumo (FHN) 神经元混沌同步. 该方法将自适应技术与反演控制方法相结合, 通过设计新型切换函数, 采用动态滑模控制律, 实现了带有不确定参数的耦合FHN神经元混沌放电同步. 研究表明该方法可以有效地削弱系统的抖振, 从而避免破坏神经元的本质特性, 且响应速度快. 仿真结果证明了该控制方法的有效性.
    In this paper, backstepping and adaptive dynamical sliding mode control is used to achieve chaos synchronization of coupled FitzHugh-Nagumo (FHN) neurons. The proposed controller consists of a combination of dynamical sliding mode control and adaptive backstepping technique. Based on a new switching function, the combined algorithm yields a design of dynamical sliding mode control law, which can realize chaos synchronization of coupled FHN neurons with uncertain parameters. It is shown that the proposed approach can effectively remove the chattering characteristic of the system, so that the intrinsic dynamics of neurons can avoid to be destroyed. Furthermore, it has rapid control performance. The simulation results have demonstrated the effectiveness of the control scheme.
    • 基金项目: 国家自然科学基金(批准号: 61072012)资助的课题.
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 61072012).
    [1]

    Manyakov N V, Van Hulle M M 2008 Chaos 18 037130

    [2]

    Elson R C, Selverston A I, Huerta R, Rulkov N F, Rabinovich M I, Abarbanel H D I 1998 Phys. Rev. Lett. 81 5692

    [3]

    Gray C M, König P, Engel A K, Singer W 1989 Nature 338 334

    [4]

    Fell J, Fernández G, Klaver P, Elger C E, Fries P 2003 Brain Res. Rev. 42 265

    [5]

    Wang Q Y, Lu Q S, Chen G R, Guo D H 2006 Phys. Lett. A 356 17

    [6]

    Singer W 1993 Annu. Rev. Physiol. 55 349

    [7]

    MacKay W A 1997 Trends Cogn. Sci. 1 176

    [8]

    Cornejo-Pérez O C, Femat R 2005 Chaos Solitons Fract. 25 43

    [9]

    Wang J, Deng B, Fei X Y 2006 Chaos Solitons Fract. 29 182

    [10]

    Che Y Q, Wang J, Tsang K M, Chan W L 2010 Nonlinear Anal. Real World Appl. 11 1096

    [11]

    Che Y Q, Wang J, Zhou S S, Deng B 2009 Chaos Solitons Fract. 40 1333

    [12]

    Sira-Ramírez H, Llanes-Santiago O 1993 Proceeding of the 32nd IEEE Conference on Decision and Control San Antonio, America, December 15-17, 1993 p1422

    [13]

    Li C H, Sun Y, Luo Q 2009 Computer Engineering and Design 30 185 (in Chinese) [李春华, 孙约, 罗琦 2009 计算机工程与设计 30 185]

    [14]

    Chen D Y, Liu Y X, Ma X Y, Zhang R F 2011 Chin. Phys. B 20 120506

    [15]

    Chao H M, Hu Y M 2001 Control and Decision 16 565 (in Chinese) [晁红敏, 胡跃明 2001 控制与决策 16 565]

    [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]

    L L, Li Y S, Wei L L, Yu M, Zhang M 2012 Acta Phys. Sin. 61 120504 (in Chinese) [吕翎, 李雨珊, 韦琳玲, 于淼, 张檬 2012 物理学报 61 120504]

    [18]

    Cao H F, Zhang R X 2011 Acta Phys. Sin. 60 050510 (in Chinese) [曹鹤飞, 张若洵 2011 物理学报 60 050510]

    [19]

    L L, Yu M, Wei L L, Zhang M, Li Y S 2012 Chin. Phys. B 21 100507

    [20]

    Chen D Y, Zhang R F, Ma X Y, Wang J 2012 Chin. Phys. B 21 120507

    [21]

    Hodgkin A L, Huxley A F 1952 J. Physiol. 117 500

    [22]

    FitzHugh R 1961 Biophys. J. 1 445

    [23]

    Thompson C J, Bardos D C, Yang Y S, Joyner K H 1999 Chaos Solitons Fract. 10 1825

    [24]

    Wang J, Deng B, Tsang K M 2004 Chaos Solitons Fract. 22 469

  • [1]

    Manyakov N V, Van Hulle M M 2008 Chaos 18 037130

    [2]

    Elson R C, Selverston A I, Huerta R, Rulkov N F, Rabinovich M I, Abarbanel H D I 1998 Phys. Rev. Lett. 81 5692

    [3]

    Gray C M, König P, Engel A K, Singer W 1989 Nature 338 334

    [4]

    Fell J, Fernández G, Klaver P, Elger C E, Fries P 2003 Brain Res. Rev. 42 265

    [5]

    Wang Q Y, Lu Q S, Chen G R, Guo D H 2006 Phys. Lett. A 356 17

    [6]

    Singer W 1993 Annu. Rev. Physiol. 55 349

    [7]

    MacKay W A 1997 Trends Cogn. Sci. 1 176

    [8]

    Cornejo-Pérez O C, Femat R 2005 Chaos Solitons Fract. 25 43

    [9]

    Wang J, Deng B, Fei X Y 2006 Chaos Solitons Fract. 29 182

    [10]

    Che Y Q, Wang J, Tsang K M, Chan W L 2010 Nonlinear Anal. Real World Appl. 11 1096

    [11]

    Che Y Q, Wang J, Zhou S S, Deng B 2009 Chaos Solitons Fract. 40 1333

    [12]

    Sira-Ramírez H, Llanes-Santiago O 1993 Proceeding of the 32nd IEEE Conference on Decision and Control San Antonio, America, December 15-17, 1993 p1422

    [13]

    Li C H, Sun Y, Luo Q 2009 Computer Engineering and Design 30 185 (in Chinese) [李春华, 孙约, 罗琦 2009 计算机工程与设计 30 185]

    [14]

    Chen D Y, Liu Y X, Ma X Y, Zhang R F 2011 Chin. Phys. B 20 120506

    [15]

    Chao H M, Hu Y M 2001 Control and Decision 16 565 (in Chinese) [晁红敏, 胡跃明 2001 控制与决策 16 565]

    [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]

    L L, Li Y S, Wei L L, Yu M, Zhang M 2012 Acta Phys. Sin. 61 120504 (in Chinese) [吕翎, 李雨珊, 韦琳玲, 于淼, 张檬 2012 物理学报 61 120504]

    [18]

    Cao H F, Zhang R X 2011 Acta Phys. Sin. 60 050510 (in Chinese) [曹鹤飞, 张若洵 2011 物理学报 60 050510]

    [19]

    L L, Yu M, Wei L L, Zhang M, Li Y S 2012 Chin. Phys. B 21 100507

    [20]

    Chen D Y, Zhang R F, Ma X Y, Wang J 2012 Chin. Phys. B 21 120507

    [21]

    Hodgkin A L, Huxley A F 1952 J. Physiol. 117 500

    [22]

    FitzHugh R 1961 Biophys. J. 1 445

    [23]

    Thompson C J, Bardos D C, Yang Y S, Joyner K H 1999 Chaos Solitons Fract. 10 1825

    [24]

    Wang J, Deng B, Tsang K M 2004 Chaos Solitons Fract. 22 469

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  • PDF下载量:  387
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-04-22
  • 修回日期:  2013-05-22
  • 刊出日期:  2013-09-05

基于反演自适应动态滑模的FitzHugh-Nagumo神经元混沌同步控制

  • 1. 电气与自动化工程学院, 天津大学, 天津 300072
    基金项目: 国家自然科学基金(批准号: 61072012)资助的课题.

摘要: 本文采用反演自适应动态滑模控制实现耦合FitzHugh-Nagumo (FHN) 神经元混沌同步. 该方法将自适应技术与反演控制方法相结合, 通过设计新型切换函数, 采用动态滑模控制律, 实现了带有不确定参数的耦合FHN神经元混沌放电同步. 研究表明该方法可以有效地削弱系统的抖振, 从而避免破坏神经元的本质特性, 且响应速度快. 仿真结果证明了该控制方法的有效性.

English Abstract

参考文献 (24)

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