On periodically intermittent stabilization of stochastic delayed neural networks

In this paper, we consider the stabilization problem of stochastic delayed neural networks (SDNNs) via periodically intermittent control. Based on a time-varying piecewise Lyapunov functional approach, a mean-square exponential stability criterion for the intermittently controlled SDNNs is firstly obtained and formulated in the form of linear matrix inequalities (LMIs). The relationship among the control period, the control width, and the upper bound on time-delay is built. Then, a sufficient condition on the existence of periodically intermittent state-feedback controllers is derived by employing the obtained stability criterion. The controller gains can be designed by solving a set of LMIs. Finally, a illustrated example is given to show the effectiveness of the proposed intermittent control method.