Global asymptotic stability of stochastic recurrent neural networks with multiple discrete delays and unbounded distributed delays

In this paper using Lyapunov–Krasovskii functional and the linear matrix inequality (LMI) approach the global asymptotic stability of stochastic recurrent neural networks with multiple discrete time-varying delays and distributed delays is analyzed. 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. Two examples are provided to illustrate the applicability of the stability results.