Artificial neural network approach to predict the electrical conductivity and density of Ag-Ni binary alloys
dc.contributor.author | Ozerdem, Mehmet Sirac | |
dc.date.accessioned | 2024-04-24T16:15:10Z | |
dc.date.available | 2024-04-24T16:15:10Z | |
dc.date.issued | 2008 | |
dc.department | Dicle Üniversitesi | en_US |
dc.description.abstract | in this study, artificial neural network (ANN) approach was done to predict electrical conductivity and density of silver-nickel binary alloys using aback-propagation neural network that uses gradient descent learning algorithm. in ANN training module, Ag%. and Ni% (weight) contents were employed as input and electrical conductivity, calculated and typical density were used as outputs. ANN system was trained using the prepared training set (also known as learning set). After training process, the test data were used to check system accuracy. As a result the neural network was found successful for the prediction of electrical conductivity and density of silver nickel binary alloys. (C) 2008 Elsevier B.V. All rights reserved. | en_US |
dc.description.sponsorship | Dicle University Research Committee [DUAPK 03-MF-86] | en_US |
dc.description.sponsorship | The author would like to thank Dicle University Research Committee since a part of this study is supported through grant DUAPK 03-MF-86. Special thanks to TUBITAK (The Scientific and Technological Research Council of Turkey) for their unfailing support to the researchers. | en_US |
dc.identifier.doi | 10.1016/j.jmatprotec.2008.01.016 | |
dc.identifier.endpage | 476 | en_US |
dc.identifier.issn | 0924-0136 | |
dc.identifier.issue | 1-3 | en_US |
dc.identifier.scopus | 2-s2.0-52949147141 | |
dc.identifier.scopusquality | Q1 | |
dc.identifier.startpage | 470 | en_US |
dc.identifier.uri | https://doi.org/10.1016/j.jmatprotec.2008.01.016 | |
dc.identifier.uri | https://hdl.handle.net/11468/15681 | |
dc.identifier.volume | 208 | en_US |
dc.identifier.wos | WOS:000260690500062 | |
dc.identifier.wosquality | Q2 | |
dc.indekslendigikaynak | Web of Science | |
dc.indekslendigikaynak | Scopus | |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Science Sa | en_US |
dc.relation.ispartof | Journal of Materials Processing Technology | |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Artificial Neural Network | en_US |
dc.subject | Electrical Conductivity | en_US |
dc.subject | Density | en_US |
dc.subject | Silver-Nickel Binary Alloys | en_US |
dc.title | Artificial neural network approach to predict the electrical conductivity and density of Ag-Ni binary alloys | en_US |
dc.title | Artificial neural network approach to predict the electrical conductivity and density of Ag-Ni binary alloys | |
dc.type | Article | en_US |