Global asymptotic stability of Markovian jumping stochastic Cohen–Grossberg BAM neural networks with discrete and distributed time-varying delays

This paper considers the problem of global asymptotic stability analysis for Markovian jumping stochastic Cohen–Grossberg BAM (MJSCGBAM) neural networks. The systems have discrete and distributed time-varying delays. Based on the stochastic stability theory, the assumption is proposed to obtain the global asymptotic stability criteria by using linear matrix inequalities, for the first time. Finally, an example is provided to illustrate the effectiveness of the theoretical results.