Synchronization for an array of coupled stochastic discrete-time neural networks with mixed delays

Abstract

In this paper, a synchronization problem is investigated for an array of coupled stochastic discrete-time neural networks with both discrete and distributed time-varying delays. By utilizing a novel Lyapunov function and the Kronecker product, it is shown that the addressed stochastic discrete-time neural networks is synchronized if certain linear matrix inequalities (LMIs) are feasible. Neither any model transformation nor free-weighting matrices are employed in the derivation of the results obtained, and they can be solved efficiently via the Matlab LMI Toolbox. The proposed synchronization criteria are less conservative than some recently known ones in the literature, which is demonstrated via two numerical examples.