This paper investigates the sampled-data synchronization issue of Markovian jumping neural networks with additive time-varying delays. Firstly, a ternary quadratic function negative-determination condition and the bilateral sampled-interval-related Lyapunov functional (BSIRLF) approach are proposed. Based on the developed two novel approaches, some new criteria based on the linear matrix inequalities (LMIs) are established to guarantee the drive-response stochastic sampled-data synchronization of Markovian jumping neural networks with additive time-varying delays. Meanwhile, the corresponding sampled-data controller gains are designed under the larger sampling interval. In the end, the availability and merits of the developed approaches are displayed via two simulative examples.

本文研究了具有附加时变延迟的马尔可夫跳跃神经网络的采样数据同步问题。首先，提出了三元二次函数负确定条件和双边采样间隔相关的李雅普诺夫函数（BSIRLF）方法。在开发的两种新方法的基础上，建立了一些基于线性矩阵不等式 (LMI) 的新标准，以保证具有附加时变延迟的马尔可夫跳跃神经网络的驱动响应随机采样数据同步。同时，在较大的采样间隔下设计了相应的采样数据控制器增益。最后，通过两个模拟示例展示了所开发方法的可用性和优点。