Generalized and optimal sequence of weights on a progressive-iterative approximation method with memory for least square fitting

被引：0

作者：

Channark, Saknarin ^{[1
,2
]}

Kumam, Poom ^{[1
,2
,3
]}

Martinez-Moreno, Juan ^{[4
]}

Chaipunya, Parin ^{[1
,2
]}

Jirakitpuwapat, Wachirapong ^{[5
]}

机构：

[1] King Mongkuts Univ Technol Thonburi KMUTT, Fac Sci, Dept Math, 126 Pracha Uthit Rd, Bangkok 10140, Thailand

[2] King Mongkuts Univ Technol Thonburi KMUTT, Fac Sci, Ctr Excellence Theoret & Computat Sci TaCS CoE, Sci Lab Bldg,126 Pracha Uthit Rd, Bangkok 10140, Thailand

[3] China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung, Taiwan

[4] Univ Jaen, Fac Expt Sci, Dept Math, Jaen, Spain

[5] Natl Sci & Technol Dev Agcy NSTDA, Natl Secur & Dual Use Technol Ctr, Pathum Thani, Thailand

来源：

MATHEMATICAL METHODS IN THE APPLIED SCIENCES | 2022年 / 45卷 / 17期

关键词：

least square fitting; progressive-iterative approximation; optimal sequence of weights; B-SPLINE CURVE; SURFACE; INTERPOLATION; CONVERGENCE; ALGORITHM; BEZIER; BASES;

D O I：

10.1002/mma.8434

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The generalized and optimal sequence of weights on a progressive-iterative approximation method with memory for least square fitting (GOLSPIA) improves the MLSPIA method by extends to the multidimensional data fitting. In addition, weights of the moving average are varied between iterations, using the three optimal sequences of weights derived from the singular values of the collocation matrix. It is proved that a series of data fitting with an appropriate alternative of weights converge to the solution of least square fitting. Moreover, the convergence rate of the new method is faster than that of the MLSPIA method. Some examples and applications in this paper show the efficiency and effectiveness of the GOLSPIA method.

引用

页码：11013 / 11030

页数：18

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