Global asymptotic stability of stochastic reaction–diffusion neural networks with time delays in the leakage terms

In this paper, a class of stochastic reaction–diffusion neural networks with time delays in the leakage terms is investigated. By using the Lyapunov functional method and linear matrix inequality (LMI) approach, sufficient conditions are derived to ensure the global asymptotic stability of an equilibrium point of the networks in the mean square. The results can be easily solved by MATLAB LMI toolbox. Finally, a numerical example is given to demonstrate the effectiveness and conservativeness of our theoretical results.

► Leakage delays are introduced into the stochastic reaction-diffusion neural networks.
► Less conservative conditions on the global stability of the system are obtained.
► The stability of the neural network is very sensitive to the leakage delays.