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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 23/2021

02.07.2021 | Original Article

Non-chattering quantized control for synchronization in finite–fixed time of delayed Cohen–Grossberg-type fuzzy neural networks with discontinuous activation

verfasst von: Chaouki Aouiti, Mayssa Bessifi

Erschienen in: Neural Computing and Applications | Ausgabe 23/2021

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Abstract

This paper investigates the controller design problem of synchronization in finite-\(\setminus \)fixed-time of a class Cohen–Grossberg-type fuzzy neural networks (CGFNNs) with discontinuous activation function and time-varying delays. By using the Lyapunov theory and differential inclusion theory, FT synchronization condition for CGF-NNs and the upper bound of the settling time for synchronization are obtained. Moreover, the settling time of FXT synchronization, that does not depend upon the initial values, is merely calculated. A novel criterion for guaranteeing the FXT synchronization of CGFNNs is derived. Our control schema achieves system synchronization within bounded time and has an advantage in convergence rate. Numerical simulations are provided to illustrate the efficaciousness of the ideal analysis.
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Metadaten
Titel
Non-chattering quantized control for synchronization in finite–fixed time of delayed Cohen–Grossberg-type fuzzy neural networks with discontinuous activation
verfasst von
Chaouki Aouiti
Mayssa Bessifi
Publikationsdatum
02.07.2021
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 23/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-021-06253-7

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