By implementing heterogeneous sampling communication mechanism, this article addresses the exponential synchronization issue of drive–response chaotic neural networks (CNNs) with interval time-varying delays by simultaneously taking into account the semi-Markovian switchings and saturating actuators. More specifically, a semi-Markovian jumping model whose transition rates (TRs) are not constant but depends on the sojourn time (ST) is introduced to characterize the stochastic changing among the interaction of CNNs, which makes the NNs model under consideration more suitable for some actual circumstances. More particularly, we assume that the sampling intervals are heterogeneous and time-varying, which may be more practical in real-life applications than homogeneous sampling policy. Additionally, by introducing some new terms, one novel time-dependent Lyapunov–Krasovskii function (LKF) is ingeniously constructed, which can fully capture the characteristic information of heterogeneous sampling pattern. Benefitting from the introduced relaxed free-weighting matrices (FWM) and resorting to the formed LKF, some sampling-interval-dependent sufficient conditions for controller design of the resulting semi-MJNNs error system are established and expressed by linear matrix inequalities (LMIs). These LMIs-based constraints can be effectively checked by utilizing the available software packages. Therein, the developed synchronization criteria dependent on both the lower and upper bounds of sampling periods, and the available information about the actual sampling pattern is fully considered. Ultimately, two numerical examples are provided to demonstrate the feasibility and practicability of our theoretical findings.

通过实现异构采样通信机制，本文通过同时考虑半马尔可夫切换和饱和执行器来解决具有间隔时变延迟的驱动器响应混沌神经网络（CNN）的指数同步问题。更具体地说，引入了一个半马尔可夫跳跃模型，该模型的过渡速率（TRs）不是恒定的，而是依赖于停留时间（ST）来表征CNN相互作用之间的随机变化，这使得所考虑的NNs模型更适合于一些实际情况。更具体地说，我们假设采样间隔是异构的且随时间变化，这在实际应用中比同质采样策略更实用。此外，通过引入一些新术语，巧妙地构造了一种新颖的时变Lyapunov–Krasovskii函数（LKF），它可以充分捕获异构采样模式的特征信息。得益于引入的松弛自由加权矩阵（FWM）并求助于形成的LKF，建立了一些采样间隔相关的充分条件，用于所得半MJNNs误差系统的控制器设计，并由线性矩阵不等式（LMI）表示。通过使用可用的软件包，可以有效地检查这些基于LMI的约束。其中，开发的同步标准取决于采样周期的上下限，并且充分考虑了有关实际采样模式的可用信息。最终，