Symbolic regression for derivation of an accurate analytical formulation using "Big Data": An application example

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dc.authorid0000-0001-5588-9407en_US
dc.contributor.authorMahouti, Peyman
dc.contributor.authorGüneş, Filiz
dc.contributor.authorBelen, Mehmet Ali
dc.contributor.authorDemirel, Salih
dc.date.accessioned2021-04-02T11:52:35Z
dc.date.available2021-04-02T11:52:35Z
dc.date.issued2017
dc.departmentAÇÜ, Mühendislik Fakültesien_US
dc.description.abstractWith emerging of the Big Data era, sample datasets are becoming increasingly large. One of the recently proposed algorithms for Big Data applications is Symbolic Regression (SR). SR is a type of regression analysis that performs a search within mathematical expression domain to generate an analytical expression that fits large size dataset. SR is capable of finding intrinsic relationships within the dataset to obtain an accurate model. Herein, for the first time in literature, SR is applied to derivate a full-wave simulation based analytical expression for the characteristic impedance Z(0) of microstrip lines using Big Data obtained from an 3D-EM simulator, in terms of only its real parameters which are substrate dielectric constant a, height h and strip width w within 1-10 GHz band. The obtained expression is compared with the targeted simulation data together with the other analytical counterpart expressions of Z(0) for different types of error function. It can be concluded that SR is a suitable algorithm for obtaining accurate analytical expressions where the size of the available data is large and the interrelations within the data are highly complex, to be used in Electromagnetic analysis and designs.
dc.identifier.citationMahouti, P., Güneş, F., Belen, M. A., & Demirel, S. (2017). Symbolic Regression for Derivation of an Accurate Analytical Formulation using “Big Data” An Application Example. Applied Computational Electromagnetics Society Journal, 32(5), 372-380.en_US
dc.identifier.endpage380en_US
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ3
dc.identifier.startpage372en_US
dc.identifier.urihttps://hdl.handle.net/11494/2915
dc.identifier.volume32en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBelen, Mehmet Ali
dc.language.isoenen_US
dc.publisherApplied Computational Electromagnetics Societyen_US
dc.relation.ispartofApplied Computational Electromagnetics Society Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBig Data applicationen_US
dc.subjectCharacteristic impedanceen_US
dc.subjectMicrostrip lineen_US
dc.subjectSymbolic Regressionen_US
dc.titleSymbolic regression for derivation of an accurate analytical formulation using "Big Data": An application exampleen_US
dc.typeArticle

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