Event-Triggered Exponential Synchronization of the Switched Neural Networks With Frequent Asynchronism

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS(2024)

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摘要
The synchronization for a class of switched uncertain neural networks (NNs) with frequent asynchronism based on event-triggered control is researched in this article. Compared with existing works that require one switching during an inter-event interval, frequent switching is allowed in this article. By employing controller-mode-dependent Lyapunov-Krasovskii functionals (LKFs), we devise the control strategy to guarantee that the switched NNs can be synchronized. The proposed LKFs can make full use of system information. Using an improved integral inequality, some sufficient stability conditions formed by linear matrix inequalities (LMIs) are derived for the synchronization of switched uncertain NNs. Average dwell time (ADT) is obtained in the form of inequality that includes the maximum inter-event interval. In addition, the existence of lower bound of inter-event interval is discussed to avoid Zeno behavior. At last, the feasibility of the proposed method is proven by a numerical example.
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关键词
Switches,Artificial neural networks,Synchronization,Delays,Control systems,Stability criteria,Numerical stability,Asynchronous switching,average dwell time (ADT),event-triggered control,exponential synchronization,neural networks (NNs)
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