Relaxed exponential stability condition for a class of uncertain time-delay neural networks

The global robust exponential stability of a class of uncertain neural networks with distributed delays is investigated in this paper. The uncertainties is in the form of polytopic type. The relaxed condition is obtained for the introduction of parameter-dependent Lypaunov-Krasovskii functionals and free-weighting matrices which 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 demonstrate the effectiveness of the proposed criteria.