This article focuses on the exponential synchronization problem of T-S fuzzy reaction-diffusion neural networks (RDNNs) with additive time-varying delays (ATVDs). Two control strategies, namely, fuzzy time sampled-data control and fuzzy time-space sampled-data control are newly proposed. Compared with some existing control schemes, the two fuzzy sampled-data control schemes cannot only tolerate some uncertainties but also save the limited communication resources for the considered systems. A new fuzzy-dependent adjustable matrix inequality technique is proposed. According to different fuzzy plant and controller rules, different adjustable matrices are introduced. In comparison with some traditional estimation techniques with a determined constant matrix, the fuzzy-dependent adjustable matrix approach is more flexible. Then, by constructing a suitable Lyapunov-Krasovskii functional (LKF) and using the fuzzy-dependent adjustable matrix approach, new exponential synchronization criteria are derived for T-S fuzzy RDNNs with ATVDs. Meanwhile, the desired fuzzy time and time-space sampled-data control gains are obtained by solving a set of linear matrix inequalities (LMIs). In the end, some simulations are presented to verify the effectiveness and superiority of the obtained theoretical results.

中文翻译：

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具有加性时变延迟的T“模糊反应”扩散神经网络的模糊采样数据控制


本文重点研究具有加性时变延迟 (ATVD) 的 TS 模糊反应扩散神经网络 (RDNN) 的指数同步问题。新提出了两种控制策略，即模糊时间采样数据控制和模糊时空采样数据控制。与一些现有的控制方案相比，两种模糊采样数据控制方案不仅可以容忍一些不确定性，而且可以为所考虑的系统节省有限的通信资源。提出了一种新的模糊相关可调矩阵不等式技术。根据不同的模糊对象和控制器规则，引入不同的可调矩阵。与一些传统的确定常数矩阵的估计技术相比，模糊相关的可调矩阵方法更加灵活。然后，通过构造合适的 Lyapunov-Krasovskii 泛函 (LKF) 并使用模糊相关可调矩阵方法，为带有 ATVD 的 TS 模糊 RDNN 导出新的指数同步准则。同时，通过求解一组线性矩阵不等式（LMI）获得所需的模糊时间和时空采样数据控制增益。最后通过仿真验证了所得理论结果的有效性和优越性。