Delay-dependent optimal guaranteed cost control of stochastic neural networks with interval nondifferentiable time-varying delays

Advances in Difference Equations

Grienggrai Rajchakit¹ 

[1] Division of Mathematics, Faculty of Science, Maejo University, Chiangmai, Thailand

关键词: stochastic neural networks;    guaranteed cost control;    mean square stabilization;    interval time-varying delays;    Lyapunov function;    linear matrix inequalities;   

DOI  :  10.1186/1687-1847-2013-241

学科分类：数学（综合）

来源: SpringerOpen

【 摘 要 】

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.

【 授权许可】

CC BY