LMI conditions for stability of stochastic recurrent neural networks with distributed delays

In this paper, the global asymptotic stability of stochastic recurrent neural networks with discrete and distributed delays is analyzed by utilizing the Lyapunov–Krasovskii functional and combining with the linear matrix inequality (LMI) approach. A new sufficient condition ensuring the global asymptotic stability for delayed recurrent neural networks is obtained in the stochastic sense using the powerful MATLAB LMI Toolbox. In addition, an example is also provided to illustrate the applicability of the result.