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

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

doi: 10.21656/1000-0887.400377
Funds:  The National Natural Science Foundation of China(61773004)
  • Received Date: 2019-12-13
  • Rev Recd Date: 2020-01-04
  • Publish Date: 2020-08-01
  • The event-based state estimation problem was investigated for a class of neural networks with mixed delays. A novel event-triggering scheme depending on both the output and exponential decay function was designed to reduce the frequency of updating. In view of both the mixed delays and the event-triggering properties, a new state estimation error system was built. The exponential stability of the error system was derived with the Lyapunov function and the inequality technique. The Zeno phenomenon was analyzed and excluded. Finally, a numerical example and its simulations were presented to illustrate the effectiveness of the proposed approach.
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  • [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.
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