Exponential stability of the neural networks with time-varying discrete and distributed delays

For a class of generalized neural networks(NNs) with discrete and distributed time-varying delays, this paper is concerned with the problems of determining the global exponential stability and estimating the exponential convergence rate. By introducing a novel Lyapunov-Krasovskii functional and some appropriate free-weighting matrices, a new delay-dependent stability criterion is derived in terms of linear matrix inequalities (LMIs). Finally, a numerical examples is given to show the superiority of the obtained results.