Delay-dependent stability for recurrent neural networks with time-varying delays

This brief is concerned with the stability for static neural networks with time-varying delays. Delay-independent conditions are proposed to ensure the asymptotic stability of the neural network. The delay-independent conditions are less conservative than existing ones. To further reduce the conservatism, delay-dependent conditions are also derived, which can be applied to fast time-varying delays. Expressed in linear matrix inequalities, both delay-independent and delay-dependent stability conditions can be checked using the recently developed algorithms. Examples are provided to illustrate the effectiveness and the reduced conservatism of the proposed result. Index Terms--Globally asymptotically stable, linear matrix inequality (LMI), local field neural network, Lyapunov functional, recurrent neural network (RNN), static neural network.