Finite-time resilient H∞ state estimation for discrete-time delayed neural networks under dynamic event-triggered mechanism. (January 2020) - Liu, Yufei; Shen, Bo; Shu, Huisheng
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.