α -Synchronization of a Class of Unbounded Delayed Inertial Cohen–Grossberg Neural Networks with Delayed Impulses

Fengjiao Zhang, Yinfang Song () and Chao Wang
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Fengjiao Zhang: School of Information and Mathematics, Yangtze University, Jingzhou 430023, China
Yinfang Song: School of Information and Mathematics, Yangtze University, Jingzhou 430023, China
Chao Wang: School of Information and Mathematics, Yangtze University, Jingzhou 430023, China

Mathematics, 2023, vol. 11, issue 19, 1-18

Abstract: As an essential dynamic behavior, the synchronization of inertial Cohen–Grossberg neural networks (ICGNNs) has received considerable attention due to its successful applications in neural cryptography, public channel cryptography, security communications, and image encryption. In this article, the α -synchronization of a class of non-autonomous unbounded delayed inertial Cohen–Gossberg neural networks with delayed impulses is investigated. Firstly, several non-autonomous impulsive differential inequalities are established, where unbounded delays, delayed impulses, and time-variable coefficients are incorporated. Subsequently, based on the proposed impulsive differential inequalities and Lyapunov function approach, the feedback controllers are designed, and some criteria for α -synchronization are provided. Finally, the validity of the presented theoretical findings is demonstrated by two specific examples. It is shown that delayed impulses can be viewed as perturbations or stabilizing sources for non-autonomous ICGNNs.

Keywords: ?-synchronization; inertial Cohen–Grossberg neural networks; unbounded delays; delayed impulses (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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