Finite-time and fixed-time stabilization of inertial memristive Cohen-Grossberg neural networks via non-reduced order method

In this paper, we focus on the finite-time and fixed-time stabilization of inertial memristive Cohen-Grossberg neural networks. To cope with the effect caused by inertial (second-order) term, most of the previous literature use the variable translation to reduce the order. Different from that, by di...

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Bibliographic Details
Published inAIMS mathematics Vol. 6; no. 7; pp. 6915 - 6932
Main Authors Wei, Ruoyu, Cao, Jinde, Qian, Wenhua, Xue, Changfeng, Ding, Xiaoshuai
Format Journal Article
LanguageEnglish
Published AIMS Press 01.01.2021
Subjects
cohen-grossberg neural networks
finite-time (fixed-time) stabilization
inertial term
mixed time delays
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ISSN2473-6988
2473-6988
DOI10.3934/math.2021405

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Summary:In this paper, we focus on the finite-time and fixed-time stabilization of inertial memristive Cohen-Grossberg neural networks. To cope with the effect caused by inertial (second-order) term, most of the previous literature use the variable translation to reduce the order. Different from that, by directly designing a Lyapunov functional and feedback controller, a novel non-reduced order method is proposed in this paper to solve the finite-time (fixed-time) stabilization problem of inertial memristive Cohen-Grossberg neural networks. Two kinds of time delays are considered in our network model, novel criteria are then derived for both cases. Lastly, numerical examples are given to verify the validity of the theoretical results.
ISSN:2473-6988
2473-6988
DOI:10.3934/math.2021405