Design of state estimator for neural networks with leakage, discrete and distributed delays

In this paper, the design problem of state estimator for neural networks with mixed time-varying delays and leakage delays has been investigated. By using appropriate model transformation that shifts the considered systems into the neutral-type time-delay systems, adapting a new Lyapunov-Krasovskii functional which takes into account the range of time-delay, and by making use of some inequality techniques, delay-dependent criteria are developed to estimate the neuron states through available output measurements such that the estimation error system is globally asymptotically stable. The criteria are formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of some standard numerical packages. Finally, three numerical examples and its simulations are given to demonstrate the usefulness and effectiveness of the presented results.