This paper investigates the issue of passivity and passification for delayed Markov jump bidirectional associate memory (BAM)-Type neural networks via non-uniform sampled-data control. By utilizing the Lyapunov–Krasovskii functional strategy, a novel delay-dependent passivity criterion is developed with respect to linear matrix inequalities to guarantee the Markov jump delayed BAM neural frameworks to be passive. At that point, in view of the got passivity conditions, the passification issue is further tackled by planning a mode-dependent non-uniform sampled-data controller design is presented. Finally, a numerical example is provided to illustrate the applicability and effectiveness of the theoretical result.

本文研究了通过非均匀采样数据控制的延迟马尔可夫跳跃双向联想记忆（BAM）型神经网络的无源性和钝化问题。通过利用Lyapunov–Krasovskii功能策略，针对线性矩阵不等式开发了一种新的依赖于延迟的无源准则，以确保Markov跳跃延迟BAM神经框架是被动的。在这一点上，鉴于获得的钝化条件，通过计划一种与模式有关的非均匀采样数据控制器设计，进一步解决了钝化问题。最后，通过数值例子说明了理论结果的适用性和有效性。