Impulsive effects on stochastic bidirectional associative memory neural networks with reaction-diffusion and leakage delays
Ganesh Kumar Thakur, Muhammed Syed Ali, Bandana Priya, V. Gokulakrishnan, Syeda Asma Kauser · International Journal of Computer Mathematics · 2021
In this paper, the problem of global asymptotic stability analysis for stochastic reaction-diffusion bidirectional associative memory neural networks (BAMNNs) with mixed delays and impulsive effects are investigated. The mixed delays consists of time delays in the leakage terms and continuously distributed delays. Based on the Lyapunov–Krasovskii functional (LKF), Ito^'s differential formula and linear matrix inequality (LMI) method, some sufficient conditions for global asymptotic stability in mean square of the equilibrium point of the systems are derived. The feasibility of the conditions are verified using the MATLAB LMI toolbox. Finally, two examples are provided to illustrate the effectiveness and validity of the derived main results.