State estimation of static neural networks with interval time-varying delays and sampled-data control

Abstract

In this paper, we consider the problem of sampled-data control for static neural networks with interval time-varying delays. As opposed to the continuous measurement, the sampled measurement is used to estimate the neuron states, and a sampled-data estimator is constructed. By converting the sampling period into a bounded time-varying delays, the error dynamics of the considered neural network is derived in terms of a dynamic system with two different time-delays. 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. Delay-dependent asymptotically stability condition is established in terms of linear matrix inequality (LMI) framework, which can be readily solved using the LMI toolbox. Finally, three examples are given to show the effectiveness of the theoretical results.