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混合时滞复值神经网络的事件触发状态估计

刘飞扬 李兵

刘飞扬,李兵. 混合时滞复值神经网络的事件触发状态估计 [J]. 应用数学和力学,2022,43(8):911-919 doi: 10.21656/1000-0887.420359
引用本文: 刘飞扬,李兵. 混合时滞复值神经网络的事件触发状态估计 [J]. 应用数学和力学,2022,43(8):911-919 doi: 10.21656/1000-0887.420359
LIU Feiyang, LI Bing. Event-Based State Estimation of Complex-Valued Neural Networks With Mixed Delays[J]. Applied Mathematics and Mechanics, 2022, 43(8): 911-919. doi: 10.21656/1000-0887.420359
Citation: LIU Feiyang, LI Bing. Event-Based State Estimation of Complex-Valued Neural Networks With Mixed Delays[J]. Applied Mathematics and Mechanics, 2022, 43(8): 911-919. doi: 10.21656/1000-0887.420359

混合时滞复值神经网络的事件触发状态估计

doi: 10.21656/1000-0887.420359
基金项目: 重庆市自然科学基金(面上项目)(cstc2019jcyj-msxmX0722);重庆市教委科技重大项目(KJZD-M202100701);重庆市创新群体项目(CXQT21021);重庆市研究生联合培养基地建设项目(JDLHPYJD2021016)
详细信息
    作者简介:

    刘飞扬(1995—),女,硕士生(E-mail:2630603244@qq.com

    李兵(1980—),男,教授,博士,硕士生导师(通讯作者. E-mail:libingcnjy@163.com)

  • 中图分类号: O357.41

Event-Based State Estimation of Complex-Valued Neural Networks With Mixed Delays

  • 摘要:

    研究了事件触发机制下混合时滞复值神经网络的状态估计问题。首先基于测量输出设计了事件触发机制,有效降低了估计器更新的频率。在触发机制中引入了等待时间,以此避免了采样中的Zeno现象。运用Lyapunov方法和复值矩阵的性质,建立了估计误差系统全局渐近稳定的充分性判据,并基于线性矩阵不等式技巧给出了复值增益矩阵

    \begin{document}$ {\boldsymbol{K}} $\end{document}

    的求解算法。最后的数值例子验证了理论成果的正确性和有效性。

  • 图  1  神经网络状态

    Figure  1.  The state of the neural network

    图  2  估计器状态

    Figure  2.  The state of the estimator

    图  3  事件触发时刻

    Figure  3.  The event trigger time

    图  4  误差系统状态

    Figure  4.  The state of the error system

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出版历程
  • 收稿日期:  2021-11-25
  • 修回日期:  2022-01-06
  • 网络出版日期:  2022-07-06
  • 刊出日期:  2022-08-01

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