On designing state estimators for discrete-time recurrent neural networks with interval-like time-varying delays

This paper deals with designing state estimators for a class of discrete-time recurrent neural networks with interval-like time-varying delays. Based on a delay bi-decomposition idea, a proper Lyapunov functional is introduced, which takes into account more information on the interval-like time-varying delay and the neuronal activation function. This Lyapunov functional, together with an improved reciprocally convex inequality, is employed to derive a sufficient condition to design suitable Luenberger-type estimators by solutions to linear matrix inequalities. An example is taken to show the effectiveness of the proposed method.