Approximation of time separating stochastic processes by neural networks revisited

George A. Anastassiou; Dimitra Kouloumpou

doi:10.26637/mjm1203/001

Abstract

Here we study the univariate quantitative approximation of time separating stochastic process over the whole real line by the normalized bell and squashing type neural network operators. Activation functions here are of compact support. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged stochastic function or its high order derivative. The approximations are pointwise and with respect to the Lp norm. The feed-forward neural networks are with one hidden layer. We finish with a great variety of special applications.

Keywords:

Time separating stochastic process, neural network approximation, modulus of continuity, activation functions of compact support, squashing functions

Mathematics Subject Classification:

40A05, 40A99, 46A70, 46A99

Authors Imprint References Funding Related Articles Downloads

George A. Anastassiou Department of Mathematical Sciences, University of Memphis, Memphis, TN 38152, U.S.A.
Dimitra Kouloumpou Section of Mathematics, Hellenic Naval Academy, Piraeus, 18539, Greece.

Pages: 233-244
Date Published: 01-07-2024
Vol. 12 No. 03 (2024): Malaya Journal of Matematik (MJM)

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