Design of Stochastic Passivity and Passification for Delayed BAM Neural Networks with Markov Jump Parameters via Non-uniform Sampled-Data Control

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.