In this paper, the problems of determining the robust exponential stability and estimating the exponential convergence rate for recurrent neural networks (RNNs) with parametric uncertainties and time-varying delay are studied. The relationship among the time-varying delay, its upper bound, and their difference is taken into account. The developed stability conditions are in terms of linear matrix inequalities (LMIs) and the integral inequality approach (IIA), which can be checked easily by recently developed algorithms solving LMIs. Furthermore, the proposed stability conditions are less conservative than some recently known ones in the literature, and this has been demonstrated via four examples with simulation.

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