• Honam Mathematical Journal
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• v.27 no.2
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• pp.225-232
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• 2005

We obtain the approximation order to a smooth function on a compact subset of $\mathbb{R}$ by generalized translation networks. In our study, the activation function is infinitely many times continuously differentiable function but it does not have special properties around ${\infty}$ and $-{\infty}$ like a sigmoidal activation function. Using the Jackson's Theorem, we get the approximation order. Especially, we obtain the approximation order by a neural network with a fixed threshold.

• Journal of applied mathematics & informatics
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• v.41 no.5
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• pp.1057-1069
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• 2023

In this paper, we have determined the degree of approximation of function belonging of Lipschitz class by using Deferred-Generalized Nörlund (D^𝛾_𝛽.N_pq) means of Fourier series and conjugate series of Fourier series, where {p_n} and {q_n} is a non-increasing sequence. So that results of DEGER and BAYINDIR [23] become special cases of our results.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.13 no.4
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• pp.257-265
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• 2009

In this paper we find an explicit form of upper bound of Hausdorff distance between given cubic spline curve and its quadratic spline approximation. As an application the approximation of offset curve of cubic spline curve is presented using our explicit error analysis. The offset curve of quadratic spline curve is exact rational spline curve of degree six, which is also an approximation of the offset curve of cubic spline curve.

• Bulletin of the Korean Mathematical Society
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• v.38 no.2
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• pp.293-302
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• 2001

We improve the greedy algorithm which is one of the general convergence criterion for certain iterative sequence in a given space by building a constructive greedy algorithm on a normed linear space using an arithmetic average of elements. We also show the degree of approximation order is still $Ο(1\sqrt{\n}$) by a bounded linear functional defined on a bounded subset of a normed linear space which offers a good approximation method for neural networks.

• Nonlinear Functional Analysis and Applications
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• v.28 no.4
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• pp.1035-1049
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• 2023

Approximation of functions of Lipschitz and Zygmund classes have been considered by various researchers under different summability means. In the proposed study, we investigated an estimation of the order of convergence of a function associated with Hardy-Littlewood series in the weighted Zygmund class W(Z^(𝜔)_r)-class by applying Euler-Hausdorff summability means and subsequently established some (presumably new) results. Moreover, the results obtained here represent the generalization of several known results.

• Kyungpook Mathematical Journal
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• v.56 no.1
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• pp.57-68
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• 2016

In this work, we shall give the degree of approximation for functions belonging to $H{\ddot{o}}lder$ class by matrix summability method of multiple Fourier series in the $H{\ddot{o}}lder$ metric.

• Journal of Applied and Pure Mathematics
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• v.4 no.5_6
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• pp.269-286
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• 2022

In this article we exhibit univariate and multivariate quantitative approximation by Kantorovich-Choquet type quasi-interpolation neural network operators with respect to supremum norm. This is done with rates using the first univariate and multivariate moduli of continuity. We approximate continuous and bounded functions on ℝ^N , N ∈ ℕ. When they are also uniformly continuous we have pointwise and uniform convergences. Our activation functions are induced by the arctangent, algebraic, Gudermannian and generalized symmetrical sigmoid functions.

• Genomics & Informatics
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• v.20 no.1
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• pp.9.1-9.6
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• 2022

Mendelian randomization (MR) uses genetic variation as a natural experiment to investigate the causal effects of modifiable risk factors (exposures) on outcomes. Two-sample Mendelian randomization (2SMR) is widely used to measure causal effects between exposures and outcomes via genome-wide association studies. 2SMR can increase statistical power by utilizing summary statistics from large consortia such as the UK Biobank. However, the first-order term approximation of standard error is commonly used when applying 2SMR. This approximation can underestimate the variance of causal effects in MR, which can lead to an increased false-positive rate. An alternative is to use the second-order approximation of the standard error, which can considerably correct for the deviation of the first-order approximation. In this study, we simulated MR to show the degree to which the first-order approximation underestimates the variance. We show that depending on the specific situation, the first-order approximation can underestimate the variance almost by half when compared to the true variance, whereas the second-order approximation is robust and accurate.

• Nonlinear Functional Analysis and Applications
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• v.27 no.4
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• pp.807-812
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• 2022

The need for smoothness measures emerged by mathematicians working in the fields of approximation theory, functional analysis and real analysis. In the present paper, a new version of generalized modulus of smoothness is studied. The aim of defining that modulus, is to find the degree of best L_p functions approximation via trigonometric polynomials. We benefit from Jackson integrals to arrive to the essential approximation theorems.

• Journal of The Korean Astronomical Society
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• v.22 no.2
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• pp.127-140
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• 1989

Problem of the diffuse radiation (DFR) transfer is solved exactly for pure hydrogen nebulae of uniform density, and accuracies of the on-the-spot (OTS) approximation are critically examined. For different values of density and spectral types of the central star, we have calculated the degree of ionization and the kinetic temperature of electrons as functions of distance from the central star, and compared them with the corresponding results of the OTS approximation. At most locations inside an HII region. the DFR ionizes considerable amount of hydrogen; therefore, the OTS approximation under-estimates the size of ionized regions. The exact treatment of the DFR transfer results in an about 10 to 20 percent increase in the classical $Str{\ddot{o}}mgren$ radius. The OTS approximation overestimates the local heating rate by raising the density of neutral hydogens. Consequently, it predicts higher values for the local electron temperature. The OTS approximation also exaggerates the dependence of electron temperature on density. When the hydrogen density is changed from $10/cm^3$ to $10^3/cm^3$ with an 06.5V star, the OTS approximation shows an about 3,000 K difference in the electron temperature, while the exact treatment of the DFR-transfer reduces the difference to about 1,000 K. The OTS approximation fails to demonstrate the brightening of the electron temperature close to the ionization boundary.