Output Synchronization via Intermittent Dynamic Event-Triggered Sampled-Data Security Control for Delayed Reaction--Diffusion Neural Networks
研究了在随机欺骗攻击和时滞下，通过间歇动态事件触发采样数据安全控制实现反应扩散神经网络的输出同步，并给出了同步判据和控制器设计方法。
This article addresses the issue of output synchronization via intermittent dynamic event-triggered sampled-data (IDETSD) security control of reaction-diffusion neural networks (RDNNs) under spatially local averaged measurements (SLAMs) subject to both delays and random deception attacks, where a Bernoulli distribution is utilized to describe whether channels suffer from the cyberattacks. An IDETSD security control method under SLAMs and random deception attacks is presented to achieve the output synchronization of delayed RDNNs. Compared with time-triggered intermittent sampled-data (SD) control strategies, a dynamic event-triggered (ET) mechanism to more effectively mitigate the impact induced by random deception attacks that intentionally tamper with the state transmission signals from sensors to controllers is introduced in this article. Moreover, new output synchronization criteria are established by applying an ET-dependent switched Lyapunov functional (LF) and inequality techniques. Then, the desired IDETSD controller is obtained by solving linear matrix inequalities (LMIs). To validate the efficacy of the proposed approach, simulation outcomes from two numerical studies are presented.