A new fixed-time stability of neural network to solve split convex feasibility problems

Autor:	Jinlan Zheng, Rulan Gan, Xingxing Ju, Xiaoqing Ou
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Neural network Finite-time stability Fixed-time stability Split convex feasibility problems Bounded linear regularity Mathematics QA1-939
Zdroj:	Journal of Inequalities and Applications, Vol 2023, Iss 1, Pp 1-21 (2023)
Druh dokumentu:	article
ISSN:	1029-242X
DOI:	10.1186/s13660-023-03046-5
Popis:	Abstract In this paper, we propose a novel neural network that achieves stability within the fixed time (NFxNN) based on projection to solve the split convex feasibility problems. Under the bounded linear regularity assumption, the NFxNN admits a solution of the split convex feasibility problem. We introduce the relationships between NFxNN and the corresponding neural networks. Additionally, we also prove the fixed-time stability of the NFxNN. The convergence time of the NFxNN is independent of the initial states. The effectiveness and superiority of the NFxNN are also demonstrated by numerical experiments compared with the other methods.
Databáze:	Directory of Open Access Journals
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