Univariate fuzzy-random neural network approximation operators

Authors:
G. A. Anastassiou
Affiliations:
-
Venue:
Computers & Mathematics with Applications
Year:
2004

Citing 2
Cited 4

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Neural Networks
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High-Order fuzzy approximation by fuzzywavelet type and neural network operators

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The errors in simultaneous approximation by feed-forward neural networks

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Multivariate stochastic Korovkin theory given quantitatively

Mathematical and Computer Modelling: An International Journal

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Abstract

In this article, we study the rate of pointwise convergence in the q-mean to thefuzzy-random unit operator of very precise univariate fuzzy-random neural network operators of cardaliaguet-Euvrard and ''squashing'' types. These fuzzy-random operators arise in a natural and common way among fuzzy-random neural networks. These rates are given through probabilistic Jackson type inequalities involving the fuzzy-random modulus of continuity of the engaged fuzzy-random function or its fuzzy derivatives. Also several interesting new results in fuzzy-random analysis are given of independent merit, which are used then in the proofs of the main results of the paper.