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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2022

04.01.2022

A New Lyapunov Function Method to the Fixed-Time Cluster Synchronization of Directed Community Networks

verfasst von: Feilong Zhou, Xiaobing Nie

Erschienen in: Neural Processing Letters | Ausgabe 3/2022

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Abstract

In this paper, the issue of fixed-time cluster synchronization is investigated for the directed community networks. We propose a new Lyapunov function method which leads to the settling time is not only independent of the initial value, but also unrelated to the number and dimensions of nodes in the networks. This means our estimated settling time is more accurate and much tighter than the one in the existing results. By designing the corresponding controllers and using the fixed-time stability theorem, some new criteria are derived to guarantee that the community networks achieve cluster synchronization in fixed time. Finally, an illustrative example with computer simulations is given to verify the theoretical analysis.
Vorheriger Artikel Existence and Finite-Time Stability of Besicovitch Almost Periodic Solutions of Fractional-Order Quaternion-Valued Neural Networks with Time-Varying Delays
Nächster Artikel Wearables and Detection of Falls: A Comparison of Machine Learning Methods and Sensors Positioning

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Metadaten
Titel
A New Lyapunov Function Method to the Fixed-Time Cluster Synchronization of Directed Community Networks
verfasst von
Feilong Zhou
Xiaobing Nie
Publikationsdatum
04.01.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10723-3

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