Brownian motion approximation by parametrized and deformed neural networks
DOI10.1007/s13398-023-01513-8zbMath1525.41009OpenAlexW4387908553MaRDI QIDQ6085364
Dimitra Kouloumpou, George A. Anastassiou
Publication date: 8 November 2023
Published in: Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales. Serie A: Matemáticas. RACSAM (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s13398-023-01513-8
Gaussian processes (60G15) Fractional processes, including fractional Brownian motion (60G22) Fractional derivatives and integrals (26A33) Inequalities in approximation (Bernstein, Jackson, Nikol'ski?-type inequalities) (41A17) Approximation by other special function classes (41A30)
Cites Work
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- Fractional neural network approximation
- Intelligent systems. Approximation by artificial neural networks
- Univariate hyperbolic tangent neural network approximation
- Multivariate hyperbolic tangent neural network approximation
- Multivariate sigmoidal neural network approximation
- On right fractional calculus
- The approximation operators with sigmoidal functions
- The analysis of fractional differential equations. An application-oriented exposition using differential operators of Caputo type
- Rate of convergence of some neural network operators to the unit-univariate case
- Intelligent computations: abstract fractional calculus, inequalities, approximations
- Intelligent systems II. Complete approximation by neural network operators
- Richards's curve induced Banach space valued multivariate neural network approximation
- Fractional Optimal Control in the Sense of Caputo and the Fractional Noether's Theorem
- Strong right fractional calculus for Banach space valued functions
- Nonlinearity: Ordinary and Fractional Approximations by Sublinear and Max-Product Operators
- A logical calculus of the ideas immanent in nervous activity
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