期刊
NEURAL NETWORKS
卷 121, 期 -, 页码 356-365出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2019.09.006
关键词
Discrete-time delayed neural networks; Dynamic event-triggered mechanism; Finite-time bounded; H-infinity performance; Resilient state estimator
资金
- National Natural Science Foundation of China [61873059, 61922024, 61673103]
- Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning of China
- Natural Science Foundation of Shanghai [18ZR1401500]
In this paper, the finite-time resilient H-infinity 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-infinity 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. (C) 2019 Elsevier Ltd. All rights reserved.
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