Neural-network-based curve fitting using totally positive rational bases

Gonzalez-Diaz, R. ; Mainar, E. (Universidad de Zaragoza) ; Paluzo-Hidalgo, E. ; Rubio, B. (Universidad de Zaragoza)

Resumen: This paper proposes a method for learning the process of curve fitting through a general class of totally positive rational bases. The approximation is achieved by finding suitable weights and control points to fit the given set of data points using a neural network and a training algorithm, called AdaMax algorithm, which is a first-order gradient-based stochastic optimization. The neural network presented in this paper is novel and based on a recent generalization of rational curves which inherit geometric properties and algorithms of the traditional rational Bézier curves. The neural network has been applied to different kinds of datasets and it has been compared with the traditional least-squares method to test its performance. The obtained results show that our method can generate a satisfactory approximation.

Idioma: Inglés

DOI: 10.3390/math8122197

Año: 2020

Publicado en: Mathematics 8, 12 (2020), 2197 [1-19]
ISSN: 2227-7390

Factor impacto JCR: 2.258 (2020)

Categ. JCR: MATHEMATICS rank: 24 / 330 = 0.073 (2020) - Q1 - T1

Factor impacto SCIMAGO: 0.495 - Mathematics (miscellaneous) (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/Construyendo Europa desde Aragón
Financiación: info:eu-repo/grantAgreement/ES/MCIU-AEI/PGC2018-096321-B-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN-FEDER/PID2019-107339GB-I00

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Matemática Aplicada (Dpto. Matemática Aplicada)