Robust state estimation for discrete-time neural networks with mixed time-delays, linear fractional uncertainties and successive packet dropouts

This paper is concerned with the robust state estimation problem for a class of discrete-time delayed neural networks with linear fractional uncertainties (LFUs) and successive packet dropouts (SPDs). The mixed time delays (MTDs) consisting of both discrete time-delays and infinite distributed delays enter into the model of the addressed neural networks. A Bernoulli distributed white sequence with a known conditional probability is introduced to govern the random occurrence of the SPDs. The main purpose of the problem under consideration is to design a state estimator such that the dynamics of the estimation error is globally asymptotically stable in the mean square. By using stochastic analysis and Lyapunov stability theory, the desired state estimator is designed to be robust against LFUs and SPDs. Finally, a simulation example is provided to show the effectiveness of the proposed state estimator design scheme.