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- W4207054772 abstract "This article proposes a data-stream-driven event-triggered control strategy using evolving fuzzy models learned by granulation of input–output samples of nonlinear systems with unknown time-varying dynamics. The evolving fuzzy model is obtained online from a data stream ensuring data coverage based on the principle of justifiable granularity and controlled by an event-triggering learning mechanism dependent on the model accuracy. This evolving fuzzy model is used to design event-triggered fuzzy controller to stabilize networked control systems while reducing the used communication resources. The event-triggered learning mechanism is employed to determine the instants in which the event-triggered fuzzy controller should be redesigned. Numerical examples illustrate the effectiveness of the proposed learning event-triggered fuzzy control algorithm." @default.
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- W4207054772 date "2022-01-23" @default.
- W4207054772 modified "2023-10-15" @default.
- W4207054772 title "Learning event‐triggered control based on evolving data‐driven fuzzy granular models" @default.
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- W4207054772 doi "https://doi.org/10.1002/rnc.6024" @default.
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