Finite-time resilient H_∞ state estimation for discrete-time delayed neural networks under dynamic event-triggered mechanism.

Abstract

In this paper, the finite-time resilient H_∞ state estimation problem is investigated for a class of discrete-time delayed neural networks. For the sake of energy saving, a dynamic event-triggered mechanism is employed in the design of state estimator for the discrete-time delayed neural networks. In order to handle the possible fluctuation of the estimator gain parameters when the state estimator is implemented, a resilient state estimator is adopted. By constructing a Lyapunov-Krasovskii functional, a sufficient condition is established, which guarantees that the estimation error system is bounded and the H_∞ performance requirement is satisfied within the finite time. Then, the desired estimator gains are obtained via solving a set of linear matrix inequalities. Finally, a numerical example is employed to illustrate the usefulness of the proposed state estimation scheme.