Relaxed delay-dependent exponential stability condition for a class of neural networks with polytopic uncertainties and distributed delays

Abstract

The global robust exponential stability of a class of neural networks with polytopic uncertainties and distributed delays is investigated in this paper. Parameter-dependent Lypaunov-Krasovskii functionals and free-weighting matrices are employed to obtain sufficient condition that guarantee the robust global exponential stability of the equilibrium point of the considered neural networks. The derived sufficient condition is proposed in terms of a set of relaxed linear matrix inequalities (LMIs), which can be checked easily by recently developed algorithms solving LMIs. A numerical example is given to demonstrate the effectiveness of the proposed criteria.