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• W3030472097 abstract "This article is concerned with the event-triggered finite-time H∞ estimator design for a class of discrete-time switched neural networks (SNNs) with mixed time delays and packet dropouts. To further reduce the data transmission, both the measured information of system outputs and switching signal of the SNNs are only allowed to be accessible for the constructed estimator at the certain triggering time instants. Under this consideration, the simultaneous presence of the switching and triggering actions also leads to the asynchronism between the indices of the SNNs and the designed estimator. Unlike the existing event-triggered strategies for the general switched linear systems, the proposed event-triggered mechanism not only allows the occurrence of multiple switches in one triggering interval but also removes the minimum dwell-time constraint on the switched signal. In light of the piecewise Lyapunov-Krasovskii functional theory, sufficient conditions are developed for the estimation error system to be stochastically finite-time bounded with a finite-time specified H∞ performance. Finally, the effectiveness and applicability of the theoretical results are verified by a switched Hopfield neural network." @default.
• W3030472097 created "2020-06-05" @default.
• W3030472097 creator A5015788725 @default.
• W3030472097 creator A5056163098 @default.
• W3030472097 date "2022-03-01" @default.
• W3030472097 modified "2023-10-13" @default.
• W3030472097 title "Finite-Time H_∞ Estimator Design for Switched Discrete-Time Delayed Neural Networks With Event-Triggered Strategy" @default.
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• W3030472097 doi "https://doi.org/10.1109/tcyb.2020.2992518" @default.
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• W3030472097 hasPublicationYear "2022" @default.
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