Global exponential stability of antiperiodic solutions for impulsive discrete-time Markovian jumping stochastic BAM neural networks with additive time-varying delays and leakage delay

摘要

This paper presents a global exponential stability of antiperiodic solution for a class of impulsive discrete-time Markovian jumping stochastic bidirectional associative memory neural networks with additive time-varying delays and leakage delay. By utilizing the Lyapunov-Krasovskii functional and contraction mapping principle, several sufficient conditions and linear matrix inequalities are derived for verifying globally exponentially stable in the mean square. There is a new delay-dependent criterion for checking the existence, uniqueness, and global stability for antiperiodic solution. Meantime, by using the numerically efficient MATLAB Toolbox, simulation examples are offered to show the effectiveness and usefulness of the obtained result.