Support vector machine model for regression applied to the estimation of the creep rupture stress in ferritic steels

Autor:	Carlos Alberto Donís-Díaz, Eduardo Valencia Morales, Carlos Morell Pérez
Jazyk:	angličtina
Rok vydání:	2013
Předmět:	Creep ferritic steels support vector machine artificial neural network Engineering (General). Civil engineering (General) TA1-2040
Zdroj:	Revista Facultad de Ingeniería Universidad de Antioquia, Iss 47 (2013)
Druh dokumentu:	article
ISSN:	0120-6230 2422-2844
DOI:	10.17533/udea.redin.16683
Popis:	Having as antecedent the use of artificial neural networks (ANN) in the estimation of the creep rupture stress in ferritic steels, new experiments have been developed using Support Vector Machine for Regression (SVMR), a recently method developed into the machine learning field. A comparative analysis between both methods established that SVMR have a better behavior in the problematic of creep. The results are explained theoretically and finally, the use of a model of SVMR that uses a polynomial kernel of third grade and a control capacity constant of 100, is proposed.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/3d865ec0a4554f68a8f3443d54431784 Zobrazit plný text záznamu View record in DOAJ