Voronovskaya type asymptotic expansions for multivariate quasi-interpolation neural network operators

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DOI:

https://doi.org/10.4067/S0719-06462014000200002

Abstract

Here we study further the multivariate quasi-interpolation of sigmoidal and hyperbolic tangent types neural network operators of one hidden layer. We derive multivariate Voronovskaya type asymptotic expansions for the error of approximation of these operators to the unit operator.

Keywords

Multivariate Neural Network Approximation , multivariate Voronovskaya type asymptotic expansion
  • George Anastassiou Department of Mathematical Sciences, University of Memphis, Memphis, TN 38152, U.S.A.
  • Pages: 33–47
  • Date Published: 2014-06-01
  • Vol. 16 No. 2 (2014): CUBO, A Mathematical Journal

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Published

2014-06-01

How to Cite

[1]
G. Anastassiou, “Voronovskaya type asymptotic expansions for multivariate quasi-interpolation neural network operators”, CUBO, vol. 16, no. 2, pp. 33–47, Jun. 2014.

Issue

Vol. 16 No. 2 (2014): CUBO, A Mathematical Journal

Section

Articles