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Neural Solution to Dynamic Overdetermined System With Applications to Data Fitting and Parameters Estimation
Liu, Mei1,2; Liufu, Ying1,2; Lu, Huiyan1; Shang, Mingsheng2
2023-08-15
摘要In recent years, dynamic overdetermined systems have sprung up and been broadly employed for handling different problems in real time. This article makes improvements in this direction by proposing, investigating, and analyzing an integral neural solution (INS) to solve the dynamic overdetermined system. Notably, an error function is constructed in the first place. Then, aided with a generated neural dynamic framework, an INS model is devised, which exploits not only saturated or even noncontinuous projection functions but also possesses noise-suppression ability with integral enhancement information. Theoretical analyses and computer simulations manifest that the proposed INS model is able to acquire the least-squares (LS) solution with superior convergence property, contrasted with the existing methods, e.g., zeroing neural network (ZNN). On top of that, applications to data fitting as well as parameters estimation ulteriorly validate the feasibility and effectiveness of the proposed INS model for handling the dynamic overdetermined system.
关键词Artificial neural networks Mathematical models Real-time systems Heuristic algorithms Convergence Computational modeling Time-varying systems Convergence property dynamic overdetermined system integral neural solution (INS) noise-suppression ability zeroing neural network (ZNN)
DOI10.1109/TSMC.2023.3285945
发表期刊IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
ISSN2168-2216
页码12
通讯作者Shang, Mingsheng(msshang@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:001051244300001
语种英语