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dc.contributor.authorUyan, Hande
dc.contributor.authorAslan, Abdullah Ozan
dc.contributor.authorKarateke, Seda
dc.contributor.authorBüyükyazıcı, İbrahim
dc.date.accessioned2024-12-02T06:07:38Z
dc.date.available2024-12-02T06:07:38Z
dc.date.issued2024en_US
dc.identifier.citationUyan, H., Aslan, A. O., Karateke, S., & Büyükyazıcı, İ. (2024). Interpolation for neural-network operators activated with a generalized logistic-type function. Journal of Inequalities and Applications, 2024(1). https://doi.org/10.1186/s13660-024-03199-xen_US
dc.identifier.issn1029-242X
dc.identifier.urihttps://hdl.handle.net/20.500.12900/452
dc.description.abstractThis paper defines a family of neural-network interpolation operators. The first derivative of generalized logistic-type functions is considered as a density function. Using the first-order uniform approximation theorem for continuous functions defined on the finite intervals, the interpolation properties of these operators are presented. A Kantorovich-type variant of the operators Fna,epsilon\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$F_{n}<^>{a,\varepsilon} $\end{document} is also introduced. The approximation of Kantorovich-type operators in LP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$L_{P}$\end{document} spaces with 1 <= p <=infinity\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$1 \leq p\leq \infty $\end{document} is studied. Further, different combinations of the parameters of our generalized logistic-type activation function theta s,a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\theta _{s, a}$\end{document} are examined to see which parameter values might give us a more efficient activation function. By choosing suitable parameters for the operator Fna,epsilon\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$F_{n}<^>{a,\varepsilon} $\end{document} and the Kantorovich variant of the operator Fna,epsilon\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$F_{n}<^>{a,\varepsilon} $\end{document}, the approximation of various function examples is studied.en_US
dc.language.isoengen_US
dc.publisherSPRINGERen_US
dc.relation.isversionof10.1186/s13660-024-03199-xen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGeneralized logistic-type functionen_US
dc.subjectNeural-Network (NN) operatorsen_US
dc.subjectInterpolationen_US
dc.subjectUniform approximationen_US
dc.subjectOrder of approximationen_US
dc.titleInterpolation for neural-network operators activated with a generalized logistic-type functionen_US
dc.typearticleen_US
dc.departmentİstanbul Atlas Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.contributor.institutionauthorKarateke, Seda
dc.identifier.volume204en_US
dc.relation.journalJOURNAL OF INEQUALITIES AND APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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