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Neural Computing and Applications
Published 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

Authors: Chaouki Aouiti, Foued Miaadi

Published in: Neural Computing and Applications | Issue 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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Metadata
Title
A new fixed-time stabilization approach for neural networks with time-varying delays
Authors
Chaouki Aouiti
Foued Miaadi
Publication date
06-11-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 8/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04586-y

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