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Periodic Event-Triggered Synchronization of Multiple Memristive Neural Networks With Switching Topologies and Parameter Mismatch
IEEE Transactions on Cybernetics ( IF 11.8 ) Pub Date : 2021-01-01 , DOI: 10.1109/tcyb.2020.2983481
Shengbo Wang , Yuting Cao , Zhenyuan Guo , Zheng Yan , Shiping Wen , Tingwen Huang

This article investigates the synchronization problem of multiple memristive neural networks (MMNNs) in the case of switching communication topologies and parameter mismatch. First, the distributed event-triggered control under continuous sampling conditions is studied. Then, a periodic event-triggered control (PETC) model is proposed to substantially reduce control consumption. Using the Lyapunov method, the properties of $M$ -matrix, and some inequalities, the sufficient criteria of synchronous control are derived. The results can be used in the analysis of other multiagent nonlinear systems. A norm-based threshold function is given to determine the update time of the controller, and it is proved that the trigger condition excludes the Zeno behavior. Subject to parameter mismatch, a quasisynchronous control strategy is proposed, which can be extended to complete synchronization provided that the system mismatch or disturbance disappears. It is worth mentioning that this article introduces the signal function into the controller, so that the theoretical error can be limited to an arbitrarily small range. Furthermore, this new controller is used in the PETC strategy which automatically avoids the Zeno behavior. Finally, one example is given to illustrate our results.

中文翻译:

具有切换拓扑和参数不匹配的多个忆阻神经网络的周期性事件触发同步

本文研究在切换通信拓扑和参数不匹配的情况下,多个忆阻神经网络(MMNN)的同步问题。首先,研究了连续采样条件下的分布式事件触发控制。然后,提出了一种周期性的事件触发控制(PETC)模型,以大幅减少控制消耗。使用Lyapunov方法,$ M $-矩阵的属性以及一些不等式,得出了同步控制的充分判据。结果可用于其他多智能体非线性系统的分析。给出了基于准则的阈值函数来确定控制器的更新时间,并证明了触发条件排除了芝诺行为。在参数不匹配的情况下,提出了一种准同步控制策略,只要系统不匹配或干扰消失,就可以扩展它以完成同步。值得一提的是,本文将信号功能引入到控制器中,以便将理论误差限制在任意小的范围内。此外,PETC策略中使用了这种新控制器,可自动避免Zeno行为。最后,给出一个例子来说明我们的结果。
更新日期:2021-01-01
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