Neural network interpolation operators of multivariate functions

被引:17
|
作者
Wang, Guoshun [1 ]
Yu, Dansheng [1 ,2 ]
Guan, Lingmin [1 ]
机构
[1] Hangzhou Normal Univ, Sch Math, Hangzhou 310036, Zhejiang, Peoples R China
[2] Hangzhou Normal Univ, Inst Adv Study Honoring Chen Jiangong, Hangzhou 310036, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Sigmoidal function; Neural network operators; Interpolation; Uniform approximate; FUNCTION APPROXIMATION; ERROR; SUPERPOSITIONS; CONVERGENCE; BOUNDS;
D O I
10.1016/j.cam.2023.115266
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this paper, we introduce a type of multivariate neural network interpolation operators Fn,sigma(f) activated by some newly defined sigmoidal functions, and give both the direct and the converse results of the approximation by Fn,sigma(f) for multivariate continuous functions. We also introduce a Kantorovich type variant of Fn,sigma(f), and establish both the direct theorem and the converse theorem of approximation by the Kantorovich type operators in Lp spaces with 1 < p < oo. Finally, we give some numerical examples to demonstrate the validity of the obtained results, and apply our operators to the image super-resolution reconstruction.(c) 2023 Elsevier B.V. All rights reserved.
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页数:22
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