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- W2974731060 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." @default.
- W2974731060 created "2019-09-26" @default.
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- W2974731060 date "2020-01-01" @default.
- W2974731060 modified "2023-10-14" @default.
- W2974731060 title "Finite-time resilient <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline id=d1e89 altimg=si4.svg><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:math> state estimation for discrete-time delayed neural networks under dynamic event-triggered mechanism" @default.
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- W2974731060 doi "https://doi.org/10.1016/j.neunet.2019.09.006" @default.
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