DELAY-DEPENDENT ROBUST STABILITY ANALYSIS FOR RECURRENT NEURAL NETWORKS WITH TIME-VARYING DELAY

In this paper, the global asymptotic stability problem is dealt with for a class of recurrent neural networks (RNNs) with time-varying delays. The time delays are not necessarily differentiable and the uncertainties are assumed to be time-varying but norm-bounded. The activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. By constructing the Lyapunov-Krasovskii functional and integral inequality approach, an improved delay-dependent stability criterion for delay RNNs is established in terms of linear matrix inequalities (LMIs). It is shown that the obtained criterion can provide less conservative results than some existing ones. Numerical examples are given to demonstrate the applicability of the proposed approach.