Curve fitting by GLSPIA

Jiayuan Zhuang, Yuanpeng Zhu and Jian Zhong

Applied Mathematics and Computation, 2024, vol. 466, issue C

Abstract: We develop the generalized B-splines least square progressive iterative approximation (GLSPIA) method to improve LSPIA method (Deng and Lin, 2014) from the perspective of its equivalent neural network framework. We replace the B-spline basis function in standard LSPIA method with the generalized B-spline basis function (Juhász and Róth, 2013) in the GLSPIA method. By adjusting the control points as well as the parameter in the core function, higher precision fitting is achieved. Moreover, in order to solve the over-fitting problem of GLSPIA method when dealing with noise data, we propose three kinds of regularizations using the geometric properties of control polygon. Numerical examples are presented to show the effectiveness of our method.

Keywords: Generalized B-spline; Geometric iterative method; Neural network; Regularization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:466:y:2024:i:c:s0096300323005969

DOI: 10.1016/j.amc.2023.128427

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