Global robust criteria for stochastic neutral neural networks with uncertainties and unbounded distributed delay

The problem of global robust stability analysis is studied for a class of stochastic neutral neural networks with uncertainties and unbounded distributed delay. Novel stability criteria are obtained in terms of linear matrix inequality (LMI) by employing the Lyapunov-Krasovskii functional method and using the free-weighting matrices technique. In addition, two examples are given to show the effectiveness of the obtained conditions.