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具有不确定性的分数阶时滞复值神经网络无源性

陈宇 周博 宋乾坤

陈宇, 周博, 宋乾坤. 具有不确定性的分数阶时滞复值神经网络无源性[J]. 应用数学和力学, 2021, 42(5): 492-499. doi: 10.21656/1000-0887.410309
引用本文: 陈宇, 周博, 宋乾坤. 具有不确定性的分数阶时滞复值神经网络无源性[J]. 应用数学和力学, 2021, 42(5): 492-499. doi: 10.21656/1000-0887.410309
CHEN Yu, ZHOU Bo, SONG Qiankun. Passivity of Fractional-Order Delayed Complex-Valued Neural Networks With Uncertainties[J]. Applied Mathematics and Mechanics, 2021, 42(5): 492-499. doi: 10.21656/1000-0887.410309
Citation: CHEN Yu, ZHOU Bo, SONG Qiankun. Passivity of Fractional-Order Delayed Complex-Valued Neural Networks With Uncertainties[J]. Applied Mathematics and Mechanics, 2021, 42(5): 492-499. doi: 10.21656/1000-0887.410309

具有不确定性的分数阶时滞复值神经网络无源性

doi: 10.21656/1000-0887.410309
基金项目: 重庆市教委科学技术研究项目(KJZDM202000701);国家自然科学基金(61773004)
详细信息
    作者简介:

    陈宇(1993—),女,硕士(E-mail: chenyucqjt@163.com);周博(1988—),男,副教授,博士(E-mail: zhoubocncq@163.com);宋乾坤(1963—),男,教授,博士(通讯作者. E-mail: qiankunsong@163.com).

  • 中图分类号: O175

Passivity of Fractional-Order Delayed Complex-Valued Neural Networks With Uncertainties

Funds: The National Natural Science Foundation of China(61773004)
  • 摘要: 该文研究了一类具有不确定性和时滞的分数阶复值神经网络无源性问题,未将复值神经网络模型拆分成两个实值系统,而是将复值系统当成一个整体直接进行处理.通过构造恰当的Lyapunov函数,并利用矩阵不等式技巧,建立了网络无源性的线性矩阵不等式判据.给出的数值例子和仿真验证了获得结论的可行性和有效性.
  • [1] HOPFIELD J J. Neural networks and physical systems with emergent collective computational abilities[J]. Proceeding of the National Academy of Sciences of the United States of America,1982,79: 2554-2558.
    [2] ARIK S. New criteria for stability of neutral-type neural networks with multiple time delays[J]. IEEE Transactions on Neural Networks and Learning Systems,2020,31: 1504-1513.
    [3] KWON O M, LEE S M, PARK J H. Improved delay-dependent exponential stability for uncertain stochastic neural networks with time-varying delays[J]. Physics Letters A,2010,374: 1232-1241.
    [4] WANG L M, HE H B, ZENG Z G. Global synchronization of fuzzy memristive neural networks with discrete and distributed delays[J]. IEEE Transactions on Fuzzy Systems,2020,28: 2022-2034.
    [5] KARIMI H R, GAO H J. New delay-dependent exponential H synchronization for uncertain neural networks with mixed time delays[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,2010,40: 173-185.
    [6] SAMLI R, YUCEL E. Global robust stability analysis of uncertain neural networks with time varying delays[J]. Neurocomputing,2015,167: 371-377.
    [7] SUNTONSINSOUNGVON E, UDPIN S. Exponential stability of discrete-time uncertain neural networks with multiple time-varying leakage delays[J]. Mathematics and Computers in Simulation,2020,171: 233-245.
    [8] BALASUBRAMANIAM P, NAGAMANI G. Passivity analysis for uncertain stochastic neural networks with discrete interval and distributed time-varying delays[J]. Journal of Systems Engineering and Electronics,2010,21(4): 688-697.
    [9] LI H Y, GAO H J, SHI P. New passivity analysis for neural networks with discrete and distributed delays[J]. IEEE Transactions on Neural Networks,2010,21: 1842-1847.
    [10] ZENG Z G, HUANG T W. New passivity analysis of continuous-time recurrent neural networks with multiple discrete delays[J]. Journal of Industrial and Management Optimization,2011,7: 283-289.
    [11] SONG Q K, CAO J D. Passivity of uncertain neural networks with both leakage delay and time-varying delay[J]. Nonlinear Dynamics,2012,67: 1695-1707.
    [12] CAO Y, SAMIDURAI R, SRIRAMAN R. Robust passivity analysis for uncertain neural networks with leakage delay and additive time-varying delays by using general activation function[J]. Mathematics and Computers in Simulation,2019,155: 57-77.
    [13] HU J, WANG J. Global stability of complex-valued recurrent neural networks with time-delays[J]. IEEE Transactions on Neural Networks and Learning Systems,2012,23: 853-865.
    [14] LI L L, SHI X H, LIANG J L. Synchronization of impulsive coupled complex-valued neural networks with delay: the matrix measure method[J]. Neural Networks,2019,117: 285-294.
    [15] LIU X W, LI Z H. Finite time anti-synchronization of complex-valued neural networks with bounded asynchronous time-varying delays[J]. Neurocomputing,2020,387: 129-138.
    [16] RAKKIYAPPAN R, SIVARANJANI K, VELMURUGAN G. Passivity and passification of memristor-based complex-valued recurrent neural networks with interval time-varying delays[J]. Neurocomputing,2014,144: 391-407.
    [17] VELMURUGAN G, RAKKIYAPPAN R, LAKSHMANAN S. Passivity analysis of memristor-based complex-valued neural networks with time-varying delays[J]. Neural Processing Letters,2015,42: 517-540.
    [18] GUO J, MENG Z D, XIANG Z R. Passivity analysis of stochastic memristor-based complex-valued recurrent neural networks with mixed time-varying delays[J]. Neural Processing Letters,2018,47: 1097-1113.
    [19] RAKKIYAPPAN R, VELMURUGAN G, CAO J D. Finite-time stability analysis of fractional-order complex-valued memristor-based neural networks with time delays[J]. Nonlinear Dynamics,2014,78: 2823-2836.
    [20] BAO H B, PARK J H, CAO J D. Synchronization of fractional-order complex-valued neural networks with time delay[J]. Neural Networks,2016,81: 16-28.
    [21] HUANG C D, CAO J D, XIAO M, et al. Bifurcations in a delayed fractional complex-valued neural network[J]. Applied Mathematics and Computation,2017,292: 210-227.
    [22] HU B X, SONG Q K, ZHAO Z J. Robust state estimation for fractional-order complex-valued delayed neural networks with interval parameter uncertainties: LMI approach[J]. Applied Mathematics and Computation,2020,373: 125033.
    [23] KILBAS A, SRIVASTAVA H M, TRUJILLO J J. Theory and Application of Fractional Differential Equations [M]. Elsevier, 2006.
    [24] SONG Q K, CAO J D. Passivity of uncertain neural networks with both leakage delay and time-varying delay[J]. Nonlinear Dynamics,2012,67: 1695-1707.
    [25] SONG Q K, YU Q Q, ZHAO Z J, et al. Boundedness and global robust stability analysis of delayed complex-valued neural networks with interval parameter uncertainties[J]. Neural Networks,2018,103: 55-62.
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出版历程
  • 收稿日期:  2020-10-15
  • 修回日期:  2020-10-21
  • 刊出日期:  2021-05-01

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