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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 8/2020

06.11.2019 | Original Article

A new fixed-time stabilization approach for neural networks with time-varying delays

verfasst von: Chaouki Aouiti, Foued Miaadi

Erschienen in: Neural Computing and Applications | Ausgabe 8/2020

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Abstract

In this article, we investigate the problem of fixed-time stabilization (FXTSB) of delayed neural networks (DNNs). Firstly, some new general conditions on the control law are established to guarantee the FXTSB of DNNs. Secondly, specific linear matrix inequalities FXTSB conditions are obtained by constructing different kinds of controller which include a delay-dependent and free ones. Furthermore, the FXTSB of DNNs with unbounded activation functions is investigated and the restriction of differentiability of the time-varying delay is removed. Finally, three numerical examples accompanied by graphical illustrations are given to illuminate our theoretical results and based on chaotic synchronization, our approach has been successfully applied to secure communication which can be realized with a time delay.
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Metadaten
Titel
A new fixed-time stabilization approach for neural networks with time-varying delays
verfasst von
Chaouki Aouiti
Foued Miaadi
Publikationsdatum
06.11.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 8/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04586-y

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