1Department of Mathematical Sciences, University of Memphis, USA
Acta mathematica Universitatis Comenianae, Tome 88 (2019) no. 1, pp. 113-130
Citer cet article
George A. Anastassiou; George A. Anastassiou. Quantitative Approximation by Kantorovich-Choquet quasi-interpolation neural network operators. Acta mathematica Universitatis Comenianae, Tome 88 (2019) no. 1, pp. 113-130. http://geodesic.mathdoc.fr/item/AMUC_2019_88_1_a9/
@article{AMUC_2019_88_1_a9,
author = {George A. Anastassiou and George A. Anastassiou},
title = { Quantitative {Approximation} by {Kantorovich-Choquet} quasi-interpolation neural network operators},
journal = {Acta mathematica Universitatis Comenianae},
pages = {113--130},
year = {2019},
volume = {88},
number = {1},
url = {http://geodesic.mathdoc.fr/item/AMUC_2019_88_1_a9/}
}
TY - JOUR
AU - George A. Anastassiou
AU - George A. Anastassiou
TI - Quantitative Approximation by Kantorovich-Choquet quasi-interpolation neural network operators
JO - Acta mathematica Universitatis Comenianae
PY - 2019
SP - 113
EP - 130
VL - 88
IS - 1
UR - http://geodesic.mathdoc.fr/item/AMUC_2019_88_1_a9/
ID - AMUC_2019_88_1_a9
ER -
%0 Journal Article
%A George A. Anastassiou
%A George A. Anastassiou
%T Quantitative Approximation by Kantorovich-Choquet quasi-interpolation neural network operators
%J Acta mathematica Universitatis Comenianae
%D 2019
%P 113-130
%V 88
%N 1
%U http://geodesic.mathdoc.fr/item/AMUC_2019_88_1_a9/
%F AMUC_2019_88_1_a9
In this article we present univariate and multivariate basic approximation by Kantorovich-Choquet type quasi-interpolation neural networkoperators with respect to supremum norm. This is done with rates using the rst univariate and multivariate moduli of continuity. We approximate continuous and bounded functions on R^N; N \subset N. When they arealso uniformly continuous we have pointwise and uniform convergences.
