Sampled-Data Control for State Estimation of Static Neural Networks

摘要

In this brief, the problem of sampled-data state estimation for static neural network is investigated. The statefeedback control design method we develop in this paper relies on the information from the sampled states. By constructing a class of Lyapunov function and combining with some inequality, a sufficient condition for the existence of state estimator is derived.