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.
@article{2063,
title = {Quantitative Approximation by a Kantorovich-Shilkret quasi-interpolation neural network operator},
journal = {CUBO, A Mathematical Journal},
volume = {20},
year = {2018},
doi = {10.4067/S0719-06462018000300001},
language = {en},
url = {http://dml.mathdoc.fr/item/2063}
}
Anastassiou, George A. Quantitative Approximation by a Kantorovich-Shilkret quasi-interpolation neural network operator. CUBO, A Mathematical Journal, Tome 20 (2018) . doi : 10.4067/S0719-06462018000300001. http://gdmltest.u-ga.fr/item/2063/