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04.09.2022

Fixed-Time Anti-synchronization and Preassigned-Time Synchronization of Discontinuous Fuzzy Inertial Neural Networks with Bounded Distributed Time-Varying Delays

verfasst von: Yang Liu, Guodong Zhang, Junhao Hu

Erschienen in: Neural Processing Letters

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Abstract

This paper is dedicated to fixed-time anti-synchronization (FXTAS) and preassigned-time synchronization (PATS) of discontinuous fuzzy inertial neural networks with mixed time-varying delays. Different from the traditional continuous neural network model, the differential inclusion theory is utilized to deal with discontinuous systems. Then, based on Lyapunov stability theory, two operational and efficient pure power-law control schemes are designed to ensure FXTAS and PATS. PATS is more flexible than fixed-time synchronization because its settling-time can be set in advance according to the actual situation. Finally, two numerical simulations are given to confirm the consistency with the theoretical results obtained in this paper.
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Metadaten
Titel
Fixed-Time Anti-synchronization and Preassigned-Time Synchronization of Discontinuous Fuzzy Inertial Neural Networks with Bounded Distributed Time-Varying Delays
verfasst von
Yang Liu
Guodong Zhang
Junhao Hu
Publikationsdatum
04.09.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-11011-4