留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

事件触发下混合时滞神经网络的状态估计

杜雨薇 李兵 宋乾坤

杜雨薇, 李兵, 宋乾坤. 事件触发下混合时滞神经网络的状态估计[J]. 应用数学和力学, 2020, 41(8): 887-898. doi: 10.21656/1000-0887.400377
引用本文: 杜雨薇, 李兵, 宋乾坤. 事件触发下混合时滞神经网络的状态估计[J]. 应用数学和力学, 2020, 41(8): 887-898. doi: 10.21656/1000-0887.400377
DU Yuwei, LI Bing, SONG Qiankun. Event-Based State Estimation for Neural Network With Time-Varying Delay and Infinite-Distributed Delay[J]. Applied Mathematics and Mechanics, 2020, 41(8): 887-898. doi: 10.21656/1000-0887.400377
Citation: DU Yuwei, LI Bing, SONG Qiankun. Event-Based State Estimation for Neural Network With Time-Varying Delay and Infinite-Distributed Delay[J]. Applied Mathematics and Mechanics, 2020, 41(8): 887-898. doi: 10.21656/1000-0887.400377

事件触发下混合时滞神经网络的状态估计

doi: 10.21656/1000-0887.400377
基金项目: 国家自然科学基金(61773004);重庆市自然科学基金(cstc2019jcyjmsxmX0722);重庆市教委科技项目(KJQN201800733);重庆市留创项目(CX2018115)
详细信息
    作者简介:

    杜雨薇(1995—),女,硕士生(E-mail: 2513958654@qq.com);李兵(1980—),男,教授,博士,硕士生导师(E-mail: libingcnjy@163.com);宋乾坤(1963—),男,教授,博士,博士生导师(通讯作者. E-mail: qiankunsong@163.com).

  • 中图分类号: O357.41

Event-Based State Estimation for Neural Network With Time-Varying Delay and Infinite-Distributed Delay

Funds: The National Natural Science Foundation of China(61773004)
  • 摘要: 研究了事件触发机制下混合时滞神经网络的状态估计问题.通过引入依赖于测量输出且具有指数衰减特性的阈值函数,设计了新的事件触发机制来降低采样和通信频率.综合混合时延和事件触发特性, 建立了新的状态估计误差系统.采用Lyapunov函数和不等式技术, 建立了误差系统指数稳定性条件, 分析并排除了事件触发机制中的Zeno现象.最后通过例子验证了理论方法的有效性.
  • [1] WANG L, SONG Q, ZHAO Z, et al. Synchronization of two nonidentical complex-valued neural networks with leakage delay and time-varying delays[J]. Neurocomputing,2019,356: 52-59.
    [2] WU H, FENG Y, TU Z, et al. Exponential synchronization of memristive neural networks with time delays[J]. Neurocomputing,2018,297: 1-7.
    [3] 舒含奇, 宋乾坤. 带有时滞的Clifford值神经网络的全局指数稳定性[J]. 应用数学和力学, 2017,38(5): 513-525.(SHU Hanqi, SONG Qiankun. Global stability of Clifford-valued recurrent neural network with mixed time-varing delays[J]. Applied Mathematics and Mechanics,2017,38(5): 513-525.(in Chinese))
    [4] 张平奎, 杨绪君. 基于激励滑模控制的分数阶神经网络的修正投影同步研究[J]. 应用数学和力学, 2018,39(3): 343-354.(ZHANG Pingkui, YANG Xujun. Modiffied projective synchronization of a class of fractional-order neural networks based on active sliding mode control[J]. Applied Mathematics and Mechanics,2018,39(3): 343-354.(in Chinese))
    [5] 闫欢, 赵振江, 宋乾坤. 具有泄漏时滞的复值神经网络的全局同步性[J]. 应用数学和力学, 2016,37(8): 832-841.(YAN Huan, ZHAO Zhenjiang, SONG Qiankun. Global synchronization of complex-valued neural network with leakage time delays[J]. Applied Mathematics and Mechanics,2016,37(8): 832-841.(in Chinese))
    [6] SHAO H, LI H, ZHU C. New stability results for delayed neural networks[J]. Applied Mathematics and Computation,2017,311: 324-334.
    [7] FUAD E, LUO Y, LIU Y, et al. State estimation for delayed neural networks with stochastic communication protocol: the finite-time case[J]. Neurocomputing,2018,281: 86-95.
    [8] ARPIT B, ARUNA T, HARSHIT B, et al. A genetically optimized neural network model for multi-class classification [J]. Expert Systems With Applications,2016,60: 211-221.
    [9] GABRIEL V, JUAN F D P, PABLO C, et al. Artificial neural networks used in optimization problems[J]. Neurcomputing,2018,272: 10-16.
    [10] MARAT A, MEHMET O. Generation of cyclic/toroidal chaos by Hopfield neural networks[J]. Neurcomputing,2014,145: 230-239.
    [11] YANG X, YUAN Q. Chaos and transient chaos in simple Hopfield neural networks[J]. Neurcomputing,2005,69(1): 232-241.
    [12] CHEN Y, LIU Q, LU R, et al. Finite-time control of switched stochastic delayed systems[J]. Neurcomputing,2016,191: 374-379.
    [13] LI X, YANG X, SONG S. Lyapunov conditions for finite-time stability of time-varying time-delay systems[J]. Automatica,2019,103: 135-140.
    [14] HU J, SUI G. Fixed-time control of static impulsive neural networks with infinite distributed delay and uncertainty[J]. Communications in Nonlinear Science and Numerical Simulation,2019,78: 104848.
    [15] ZHOU J, ZHAO T. State estimation for neural networks with two additive time-varying delay components using delay-product-type augmented Lyapunov-Krasovskii functionals[J]. Neurocomputing,2019,350: 155-169.
    [16] LIU Y, SHEN B, LI Q. State estimation for neural networks with Markov-based nonuniform sampling: the partly unknown transition probability case[J]. Neurocomputing,2019,357: 261-270.
    [17] LI Q, ZHU Q, ZHONG S, et al. State estimation for uncertain Markovian jump neural networks with mixed delays[J]. Neurocomputing,2016,182: 82-93.
    [18] SYED A M, SARAVANAN S, ARIK S. Finite-time H state estimation for switched neural networks with time-varying delays[J]. Neurocomputing,2016,207: 580-589.
    [19] TAE H, JU H, HOYOUL J. Network-based H state estimation for neural networks using imperfect measurement[J]. Applied Mathematics and Computation,2018,316: 205-214.
    [20] DONG H, WANG Z, SHEN B, et al. Variance-constrained H control for a class of nonlinear stochastic discrete time-varying systems: the event-triggered design[J]. Automatica,2016,72: 28-36.
    [21] LIU Y, WANG Z, HE X, et al. Event-triggered least squares fault estimation with stochastic nonlinearities[J]. IFAC Proceedings Volumes,2014,47(3): 1855-1860.
    [22] XIE Y, LIN Z. Event-triggered global stabilization of general linear systems with bounded controls[J]. Automatica,2019,107: 241-254.
    [23] SUN Y, YANG G. Event-triggered state estimation for networked control systems with lossy network communication[J]. Information Sciences,2019,492: 1-12.
    [24] LIU D, YANG G. Robust event-triggered control for networked control systems[J]. Information Sciences,2018,459: 168-197.
    [25] WANG Z, HU J, MA L. Event-based distributed information fusion over sensor networks[J]. Information Fusion,2018,39: 53-55.
    [26] YU H, HE Y, WU M. Delay-dependent state estimation for neural networks with time-varying delay[J]. Neurocomputing,2018,275: 881-887.
    [27] WANG Z, LIU Y, LIU X. State estimation for jumping recurrent neural networks with discrete and distributed delays[J]. Neural Networks,2009,22(1): 41-48.
    [28] ZHANG W, WANG Z, LIU Y, et al. Event-based state estimation for a class of complex networks with time-varying delays: a comparison principle approach[J]. Physics Letters A,2017,381(1): 10-18.
    [29] YANG W, LEI L, YANG C. Event-based distributed state estimation under deception attack[J]. Neurocomputing,2017,270: 145-151.
    [30] SHI D, CHEN T, MOHAMED D. Event-based state estimation of linear dynamic systems with unknown exogenous inputs[J]. Automatica,2016,69: 275-288.
    [31] GUAN Z, DAVID J H, SHEN X. On hybrid impulsive and switching systems and application to nonlinear control[J]. IEEE Transactions on Automatic Control,2005,50(7): 1058-1062.
    [32] BOYD S, EL GHAOUI L, FERON E, et al. Linear Matrix Inequalities in System and Control Theory [M]. Society for Industrial and Applied Mathematics, 1994.
  • 加载中
计量
  • 文章访问数:  1239
  • HTML全文浏览量:  176
  • PDF下载量:  338
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-12-13
  • 修回日期:  2020-01-04
  • 刊出日期:  2020-08-01

目录

    /

    返回文章
    返回