An improved fixed-time stabilization problem of delayed coupled memristor-based neural networks with pinning control and indefinite derivative approach

Autor:	Chao Yang, Juntao Wu, Zhengyang Qiao
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	fixed-time stabilization memristor nonlinear coupling indefinite derivative Mathematics QA1-939 Applied mathematics. Quantitative methods T57-57.97
Zdroj:	Electronic Research Archive, Vol 31, Iss 5, Pp 2428-2446 (2023)
Druh dokumentu:	article
ISSN:	2688-1594
DOI:	10.3934/era.2023123?viewType=HTML
Popis:	In this brief, we propose a class of generalized memristor-based neural networks with nonlinear coupling. Based on the set-valued mapping theory, novel Lyapunov indefinite derivative and Memristor theory, the coupled memristor-based neural networks (CMNNs) can achieve fixed-time stabilization (FTS) by designing a proper pinning controller, which randomly controls a small number of neuron nodes. Different from the traditional Lyapunov method, this paper uses the implementation method of indefinite derivative to deal with the non-autonomous neural network system with nonlinear coupling topology between different neurons. The system can obtain stabilization in a fixed time and requires fewer conditions. Moreover, the fixed stable setting time estimation of the system is given through a few conditions, which can eliminate the dependence on the initial value. Finally, we give two numerical examples to verify the correctness of our results.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/4171234128f04f3db29c1adf89ce2c7d Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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