Progressive Iterative Approximation Method with Memory and Sequences of Weights for Least Square Curve Fitting

Journal article

Authors/Editors

Strategic Research Themes

Modeling Design and Optimization (Computational Science and Engineering)

Publication Details

Author list: Channark, Saknarin; Kumam, Poom; Chaipunya, Parin; Jirakitpuwapat, Wachirapong;

Publication year: 2023

Volume number: 28

Issue number: 1

Start page: 90

End page: 107

Number of pages: 18

ISSN: 2586-9000

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151531313&partnerID=40&md5=11500dcb1937ce5ce7ba24ef410082fe

Languages: English-Great Britain (EN-GB)

Abstract

The progressive iterative approximation method with memory and sequences of weights for least square curve fitting (SSLSPIA) is presented in this paper. This method improves the MLSPIA method by varying the weights of the moving average between it-erations, using three sequences of weights derived from the singular values of a colloca-tion matrix. It is proved that a sequence of fitting curves with an appropriate alternative of weights converge to the solution of least square fitting and that the convergence rate of the new method is faster than that of the MLSPIA method. Some examples and applications in this paper prove the SSLSPIA method is superior. © 2023, Thammasat University. All rights reserved.

Keywords

Least square curve fitting, Sequences of weights