Results 1 to 10 of about 32 (32)

Comparative study on Fractional Isothermal Chemical Model

open access: yesAlexandria Engineering Journal, 2021
This article investigates a family of approximate solutions for the fractional isothermal chemical (FIC) equation based on mass action kinetics for autocatalytic feedback, involving the conversion of a reactant in the Liouville-Caputo sense. We apply two
Khaled M. Saad
doaj   +1 more source

Hyperbolic Tangent Like Relied Banach Space Valued Neural Network Multivariate Approximations

open access: yesAnnals 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   +1 more source

Sigmoid functions for the smooth approximation to the absolute value function

open access: yesMoroccan Journal of Pure and Applied Analysis, 2021
We present smooth approximations to the absolute value function |x| using sigmoid functions. In particular, x erf(x/μ) is proved to be a better smooth approximation for |x| than x tanh(x/μ) and x2+μ\sqrt {{x^2} + \mu } with respect to accuracy.
Bagul Yogesh J., Chesneau Christophe
doaj   +1 more source

New fractional derivative with non-singular kernel for deriving Legendre spectral collocation method

open access: yesAlexandria Engineering Journal, 2020
Fractional derivative models with an Abdon-Baleanu-Caputo (ABC) fractional derivative with a non-singular Mittag-Leffler kernel in the Liouville-Caputo (LC) sense are investigated using a spectral collocation method based on Legendre approximations. This
Khaled M. Saad
doaj   +1 more source

Perturbation of the one-dimensional time-independent Schrödinger equation with a rectangular potential barrier

open access: yesOpen Mathematics, 2020
In Applied Mathematics Letters 74 (2017), 147–153, the Hyers-Ulam stability of the one-dimensional time-independent Schrödinger equation was investigated when the relevant system has a potential well of finite depth. As a continuous work,
Jung Soon-Mo, Choi Ginkyu
doaj   +1 more source

A General Approximation Approach for the Simultaneous Treatment of Integral and Discrete Operators

open access: yesAdvanced Nonlinear Studies, 2018
In this paper, we give a unitary approach for the simultaneous study of the convergence of discrete and integral operators described by means of a family of linear continuous functionals acting on functions defined on locally compact Hausdorff ...
Vinti Gianluca, Zampogni Luca
doaj   +1 more source

Quantitative estimates for perturbed sampling Kantorovich operators in Orlicz spaces

open access: yesDemonstratio Mathematica
In the present work, we establish a quantitative estimate for the perturbed sampling Kantorovich operators in Orlicz spaces, in terms of the modulus of smoothness, defined by means of its modular functional.
Costarelli Danilo   +2 more
doaj   +1 more source

Efficient α-Dense Curve Strategies for Multiple Integrals over Hyper-rectangle Regions

open access: yesAnnals of the West University of Timisoara: Mathematics and Computer Science
In this paper, we propose an approximation technique to compute multiple integrals of a non-negative real continuous function over a hyper-rectangle Ω of ℝn.
Rahal Mohamed, Guettal Djaouida
doaj   +1 more source

Unique compact representation of magnetic fields using truncated solid harmonic expansions

open access: yesEuropean Journal of Applied Mathematics
Precise knowledge of magnetic fields is crucial in many medical imaging applications such as magnetic resonance imaging (MRI) or magnetic particle imaging (MPI), as they form the foundation of these imaging systems. Mathematical methods are essential for
Marija Boberg   +2 more
doaj   +1 more source

The mathematics of adversarial attacks in AI – why deep learning is unstable despite the existence of stable neural networks

open access: yesEuropean Journal of Applied Mathematics
The unprecedented success of deep learning (DL) makes it unchallenged when it comes to classification problems. However, it is well established that the current DL methodology produces universally unstable neural networks (NNs).
Alexander Bastounis   +2 more
doaj   +1 more source

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