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

George A. Anastassiou

doi:10.4067/S0719-06462018000300001

DOI:

https://doi.org/10.4067/S0719-06462018000300001

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 â„^N, N âˆˆ â„•. When they are additionally uniformly continuous we derive pointwise and uniform convergences.

Keywords

error function based activation function , multivariate quasi-interpolation neural network approximation , Kantorovich-Shilkret type operator

George A. Anastassiou Department of Mathematical Sciences, University of Memphis, Memphis, TN 38152, U.S.A.

Pages: 01–11
Date Published: 2019-03-15
Vol. 20 No. 3 (2018)

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