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

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DOI:

https://doi.org/10.4067/S0719-06462012000300005

Abstract

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

Keywords

Neural Network Fractional Approximation , Voro- novskaya Asymptotic Expansion , fractional derivative
  • Pages: 71–83
  • Date Published: 2012-10-01
  • Vol. 14 No. 3 (2012): CUBO, A Mathematical Journal

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Published

2012-10-01

How to Cite

[1]
G. A. Anastassiou, “Fractional Voronovskaya type asymptotic expansions for quasi-interpolation neural network operators”, CUBO, vol. 14, no. 3, pp. 71–83, Oct. 2012.

Issue

Vol. 14 No. 3 (2012): CUBO, A Mathematical Journal

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Articles

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