Sampled-data state estimation of Markovian jump static neural networks with interval time-varying delays

Abstract

In this paper, we consider the problem of sampled-data state estimation of Markovian jump delayed static neural networks. By constructing a suitable Lyapunov–Krasovskii functional with double and triple integral terms and using Jensen inequality, delay-dependent criteria are presented so that the error system is asymptotically stable. Instead of the continuous measurement, the sampled measurement is employed to estimate the neuron states. It is further demonstrated that the configuration of the gain matrix of state estimator is changed to find a feasible solution of a linear matrix inequalities, which is efficiently facilitated by available algorithms. Finally, two numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results.