Stability analysis of discrete-time stochastic neural networks with time-varying delays

This paper investigates the problem of stability analysis for a class of discrete-time stochastic neural networks (DSNNs) with time-varying delays. In the concerned model, stochastic disturbances are described by a Brownian motion, and time-varying delay d(k)d(k) satisfies dm≤d(k)≤dMdm≤d(k)≤dM. Based on the delay partitioning idea and some inequalities, a new stability criterion with less conservatism in terms of linear matrix inequalities (LMIs) is proposed by introducing a novel Lyapunov–Krasovskii functional combined with a free-weighting matrix method. The condition can be checked by utilizing some numerical software and a numerical example is provided to show the usefulness of the proposed condition.