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 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
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.

## 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

###### 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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