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

Journal of Inequalities and Applications

Research

Rulan Gan¹  Xingxing Ju²  Xiaoqing Ou³  Jinlan Zheng⁴ 

[1] College of Computer and Information Science, Southwest University, 400715, Chongqing, China;College of Electronics and Information Engineering, Sichuan University, Chengdu, China;College of Management, Chongqing College of Humanities, Science & Technology, 401524, Chongqing, China;School of Mathematics and Statistics, Southwest University, 400715, Chongqing, China;School of Mathematics and Statistics, Northeast Normal University, 130024, Changchun, China;

关键词: Neural network;    Finite-time stability;    Fixed-time stability;    Split convex feasibility problems;    Bounded linear regularity;   

DOI  :  10.1186/s13660-023-03046-5

 received in 2023-03-08, accepted in 2023-10-09,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023

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