Global exponential dissipativity in mean of stochastic neural networks with infinity distributed delays

In this paper, we investigate the problem on global exponential dissipativity in mean of stochastic neural networks with infinity distributed delays. By employing a new stochastic delay differential inequality which improve and extend the classical Halanay inequality, and exploiting the linear matrix inequality (LMI) approach, the sufficient easy-to-test conditions for the global exponential dissitivity in mean is established. Meanwhile, the estimation of global exponential attractive set in mean is given out. Finally, an examples with numerical simulations is presented and analyzed to demonstrate the obtained result.