Quantitative Approximation by a Kantorovich-Shilkret quasi-interpolation neural network operator

dc.creator	Anastassiou, George A.
dc.date	2019-03-15
dc.identifier	http://revistas.ufro.cl/ojs/index.php/cubo/article/view/2063
dc.identifier	10.4067/S0719-06462018000300001
dc.description	In this article we present multivariate basic approximation by a Kantorovich-Shilkret type quasi-interpolation neural network operator with respect to supremum norm. This is done with rates using the multivariate modulus of continuity. We approximate continuous and bounded functions on ℝN, N ∈ ℕ. When they are additionally uniformly continuous we derive pointwise and uniform convergences.	en-US
dc.format	application/pdf
dc.language	eng
dc.publisher	Universidad de La Frontera. Temuco, Chile.	en-US
dc.relation	http://revistas.ufro.cl/ojs/index.php/cubo/article/view/2063/1846
dc.source	CUBO, A Mathematical Journal; Vol. 20 No. 3 (2018); 01–11	en-US
dc.source	CUBO, A Mathematical Journal; Vol. 20 Núm. 3 (2018); 01–11	es-ES
dc.source	0719-0646
dc.source	0716-7776
dc.subject	error function based activation function	en-US
dc.subject	multivariate quasi-interpolation neural network approximation	en-US
dc.subject	Kantorovich-Shilkret type operator	en-US
dc.title	Quantitative Approximation by a Kantorovich-Shilkret quasi-interpolation neural network operator	en-US
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion