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

Abstract 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,...

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Autor principal:	Anastassiou,George A.
Lenguaje:	English
Publicado:	Universidad de La Frontera. Departamento de Matemática y Estadística. 2018
Materias:	error function based activation function multivariate quasi-interpolation neural network approximation Kantorovich-Shilkret type operator
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0719-06462018000300001
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spelling	oai:scielo:S0719-064620180003000012019-03-08Quantitative Approximation by a Kantorovich-Shilkret quasi-interpolation neural network operatorAnastassiou,George A. error function based activation function multivariate quasi-interpolation neural network approximation Kantorovich-Shilkret type operator Abstract 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.info:eu-repo/semantics/openAccessUniversidad de La Frontera. Departamento de Matemática y Estadística.Cubo (Temuco) v.20 n.3 20182018-10-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0719-06462018000300001en10.4067/S0719-06462018000300001
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	error function based activation function multivariate quasi-interpolation neural network approximation Kantorovich-Shilkret type operator
spellingShingle	error function based activation function multivariate quasi-interpolation neural network approximation Kantorovich-Shilkret type operator Anastassiou,George A. Quantitative Approximation by a Kantorovich-Shilkret quasi-interpolation neural network operator
description	Abstract 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.
author	Anastassiou,George A.
author_facet	Anastassiou,George A.
author_sort	Anastassiou,George A.
title	Quantitative Approximation by a Kantorovich-Shilkret quasi-interpolation neural network operator
title_short	Quantitative Approximation by a Kantorovich-Shilkret quasi-interpolation neural network operator
title_full	Quantitative Approximation by a Kantorovich-Shilkret quasi-interpolation neural network operator
title_fullStr	Quantitative Approximation by a Kantorovich-Shilkret quasi-interpolation neural network operator
title_full_unstemmed	Quantitative Approximation by a Kantorovich-Shilkret quasi-interpolation neural network operator
title_sort	quantitative approximation by a kantorovich-shilkret quasi-interpolation neural network operator
publisher	Universidad de La Frontera. Departamento de Matemática y Estadística.
publishDate	2018
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0719-06462018000300001
work_keys_str_mv	AT anastassiougeorgea quantitativeapproximationbyakantorovichshilkretquasiinterpolationneuralnetworkoperator
_version_	1714206800440459264

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

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