Finite-time resilient H∞ state estimation for discrete-time delayed neural networks under dynamic event-triggered mechanism. (January 2020)

• Record Type: Journal Article
• Authors: Liu, Yufei; Shen, Bo; Shu, Huisheng
• Is Part Of: Neural networks. Volume 121(2020)
• Page Start: 356
• Page End: 365
• Publication Date: 2020-01
• Subjects: Discrete-time delayed neural networks -- Dynamic event-triggered mechanism -- Finite-time bounded -- H∞ performance -- Resilient state estimator; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2019.09.006
• Languages: English
• ISSNs: 0893-6080

Abstract

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.