Exponential state estimator design for discrete‐time neural networks with discrete and distributed time‐varying delays

In this article, the probl of state estimation for discrete‐time neural networks with mixed time‐varying delays is investigated. The mixed time delays consist of both discrete and distributed delays. An appropriate Lyapunov–Krasovskii functional put forward to reflect the mixed time‐varying delays is proposed to establish sufficient conditions for the existence of admissible state estimators. The conditions are described in the form of linear matrix inequalities (LMIs), which guarantee the estimation error to be globally exponentially stable in the presence of mixed time‐varying delays. Then, the desired estimator matrix gain can be characterized in terms of the solution to these LMIs. A numerical example is addressed to show the effectiveness of the proposed design method.