State estimation of recurrent neural networks with interval time-varying delay: an improved delay-dependent approach

Abstract

This paper is concerned with the state estimation problem for a class of recurrent neural networks with interval time-varying delay, where time delay includes either slow or fast time-varying delay. A novel delay-dependent criterion, in which the rate–range of time delay is also considered, is established to estimate the neuron states through available output measurements such that, for all admissible time delays, the dynamics of the estimation error system is globally asymptotically stable. The proposed method is based on a new Lyapunov–Krasovskii functional with triple-integral terms and free-weighting matrix approach. Numerical examples are given to illustrate the effectiveness of the method.