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Richards's curve induced Banach space valued multivariate neural network approximation. [PDF]

Arab J Math, 2023
AbstractHere, we present multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or $${\mathbb {R}}^{N},$$ R N ,
Anastassiou GA, Karateke S.
europepmc   +5 more sources

General multivariate arctangent function activated neural network approximations

Journal of Numerical Analysis and Approximation Theory, 2022
Here we expose multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or \(\mathbb{R}^{N}\), \(N\in \mathbb{N}\), by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature ...
George A. Anastassiou
doaj   +2 more sources

Multivariate hyperbolic tangent neural network approximation

Computers and Mathematics With Applications, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
George A Anastassiou
exaly   +4 more sources

Multivariate error function based neural network approximations

Journal of Numerical Analysis and Approximation Theory, 2014
Here we present multivariate quantitative approximations of real and complex valued continuous multivariate functions on a box or \(\mathbb{R}^{N},\) \(N\in \mathbb{N}\), by the multivariate quasi-interpolation, Baskakov type and quadrature type neural ...
George A. Anastassiou
doaj   +5 more sources

Hyperbolic Tangent Like Relied Banach Space Valued Neural Network Multivariate Approximations

Annals of the West University of Timisoara: Mathematics and Computer Science, 2023
Here we examine the multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or ℝN , N ∈ ℕ, by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network ...
Anastassiou George A.
doaj   +3 more sources

Physics-informed neural networks with hybrid Kolmogorov-Arnold network and augmented Lagrangian function for solving partial differential equations [PDF]

Scientific Reports
Physics-informed neural networks (PINNs) have emerged as a fundamental approach within deep learning for the resolution of partial differential equations (PDEs).
Zhaoyang Zhang, Qingwang Wang, Yinxing Zhang, Tao Shen, Weiyi Zhang   +4 more
doaj   +2 more sources

Multiple-Composite Quantitative Approximation by Multivariate Kantorovich–Choquet Neural Networks

Mathematics
In this work we study the univariate and multivariate quantitative approximation by multi-composite Kantorovich–Choquet-type quasi-interpolation neural network operators with respect to the supremum norm.
George A. Anastassiou
doaj   +2 more sources

Multiple general sigmoids based Banach space valued neural network multivariate approximation

Cubo, 2023
Here we present multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or \(\mathbb{R}^{N},\) \(N\in \mathbb{N}\), by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature ...
George A. Anastassiou
doaj   +1 more source

Comparison of bayesian regularized neural network, random forest regression, support vector regression and multivariate adaptive regression splines algorithms to predict body weight from biometrical measurements in thalli sheep [PDF]

Kafkas Universitesi Veteriner Fakültesi Dergisi, 2022
In this study, it is aimed to compare several data mining and artificial neural network algorithms to predict body weight from biometric measurements for the Th alli sheep breed.
Cem TIRINK
doaj   +1 more source

Neural Network Approximation for Time Splitting Random Functions

Mathematics, 2023
In this article we present the multivariate approximation of time splitting random functions defined on a box or RN,N∈N, by neural network operators of quasi-interpolation type.
George A. Anastassiou, Dimitra Kouloumpou   +1 more
doaj   +1 more source