Delay-dependent optimal guaranteed cost control of stochastic neural networks with interval nondifferentiable time-varying delays
Grienggrai Rajchakit · Advances in Difference Equations · 2013
This paper studies the problem of a guaranteed cost control for a class of stochastic delayed neural networks. The time delay is a continuous function belonging to a given interval, but it is not necessarily differentiable. A cost function is considered as a nonlinear performance measure for the closed-loop system. The stabilizing controllers to be designed must satisfy some mean square exponential stability constraints on the closed-loop poles. By constructing a set of augmented Lyapunov-Krasovskii functional, a guaranteed cost controller is designed via memory less state feedback control, and new sufficient conditions for the existence of the guaranteed cost state-feedback for the system are given in terms of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the obtained result.