事件触发控制下参数扰动的惯性 memristive 神经网络鲁棒指数同步。
Event-triggered control for robust exponential synchronization of inertial memristive neural networks under parameter disturbance.
机构信息
School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China; Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science and Technology, Changsha, 410114, China.
College of Information Science and Engineering, Hunan University, Changsha, 410082, China.
出版信息
Neural Netw. 2023 Jul;164:67-80. doi: 10.1016/j.neunet.2023.04.024. Epub 2023 Apr 26.
Synchronization of memristive neural networks (MNNs) by using network control scheme has been widely and deeply studied. However, these researches are usually restricted to traditional continuous-time control methods for synchronization of the first-order MNNs. In this paper, we study the robust exponential synchronization of inertial memristive neural networks (IMNNs) with time-varying delays and parameter disturbance via event-triggered control (ETC) scheme. First, the delayed IMNNs with parameter disturbance are changed into first-order MNNs with parameter disturbance by constructing proper variable substitutions. Next, a kind of state feedback controller is designed to the response IMNN with parameter disturbance. Based on feedback controller, some ETC methods are provided to largely decrease the update times of controller. Then, some sufficient conditions are provided to realize robust exponential synchronization of delayed IMNNs with parameter disturbance via ETC scheme. Moreover, the Zeno behavior will not happen in all ETC conditions shown in this paper. Finally, numerical simulations are given to verify the advantages of the obtained results such as anti-interference performance and good reliability.
利用网络控制方案实现忆阻神经网络（MNN）的同步已经得到了广泛而深入的研究。然而，这些研究通常仅限于传统的用于一阶 MNN 同步的连续时间控制方法。本文研究了时变时滞和参数扰动的惯性忆阻神经网络（IMNN）的鲁棒指数同步问题，通过事件触发控制（ETC）方案实现。首先，通过构建适当的变量替换，将具有参数扰动的时滞 IMNN 转化为具有参数扰动的一阶 MNN。然后，针对具有参数扰动的响应 IMNN 设计了一种状态反馈控制器。基于反馈控制器，提出了一些 ETC 方法，以大大减少控制器的更新次数。然后，给出了一些充分条件，以通过 ETC 方案实现具有参数扰动的时滞 IMNN 的鲁棒指数同步。此外，在本文给出的所有 ETC 条件下，都不会发生 Zeno 行为。最后，通过数值模拟验证了所得到的结果的优势，如抗干扰性能和良好的可靠性。
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