A comparison of genetic programming and neural networks; new formulations for electrical resistivity of Zn-Fe alloys

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dc.authoridErkayman, Burak/0000-0002-9551-2679
dc.authoridOZDEMIR, RASIM/0000-0003-1439-0444
dc.contributor.authorKarahan, Ismail Hakki
dc.contributor.authorOzdemir, Rasim
dc.contributor.authorErkayman, Burak
dc.date.accessioned2024-09-18T21:02:54Z
dc.date.available2024-09-18T21:02:54Z
dc.date.issued2013
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractIt is difficult to automatically solve a problem in a systematic method without using computers. In this study, a comparison between Neural Network (NN) and genetic programming (GEP) soft computing techniques as alternative tools for the formulation of electrical resistivity of zinc-iron (Zn-Fe) alloys for various compositions is proposed. Different formulations are supplied to control the verity and robustness of NN and GEP for the formulation to design composition and electrolyte conditions in certain ranges. The input parameters of the NN and GEP models are weight percentages of zinc and iron in the film and in the electrolyte, measurement temperature, and corrosion voltage of the films. The NN- and GEP-based formulation results are compared with experimental results and found to be quite reliable with a very high correlation (R (2)=0.998 for GEP and 0.999 for NN).en_US
dc.identifier.doi10.1007/s00339-013-7544-3
dc.identifier.endpage476en_US
dc.identifier.issn0947-8396
dc.identifier.issn1432-0630
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-84900569020en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage459en_US
dc.identifier.urihttps://doi.org/10.1007/s00339-013-7544-3
dc.identifier.urihttps://hdl.handle.net/20.500.12483/13125
dc.identifier.volume113en_US
dc.identifier.wosWOS:000325115900029en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofApplied Physics A-Materials Science & Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElectrodepositionen_US
dc.subjectPredictionen_US
dc.subjectSteelen_US
dc.subjectNien_US
dc.titleA comparison of genetic programming and neural networks; new formulations for electrical resistivity of Zn-Fe alloysen_US
dc.typeArticleen_US

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