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

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

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Published

2019-03-15

How to Cite

[1]
G. A. Anastassiou, “Quantitative Approximation by a Kantorovich-Shilkret quasi-interpolation neural network operator”, CUBO, vol. 20, no. 3, pp. 01–11, Mar. 2019.

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Vol. 20 No. 3 (2018)

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Articles