ARTIFICIAL-NEURAL-NETWORK PREDICTION OF HEXAGONAL LATTICE PARAMETERS FOR NON-STOICHIOMETRIC APATITES

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dc.authorid0000-0001-9517-7957
dc.contributor.authorKockan, Umit
dc.contributor.authorOzturk, Fahrettin
dc.contributor.authorEvis, Zafer
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2014
dc.departmentNiğde ÖHÜ
dc.description.abstractIn this study, hexagonal lattice parameters (a and c) and unit-cell volumes of non-stoichiometric apatites of M-10(TO4)(6)X-2 are predicted from their ionic radii with artificial neural networks. A multilayer-perceptron network is used for training. The results indicate that the Bayesian regularization method with four neurons in the hidden layer with a tansig activation function and one neuron in the output layer with a purelin function gives the best results. It is found that the errors for the predicted data of the lattice parameters of a and c are less than 1 % and 2 %, respectively. On the other hand, about 3 % errors were encountered for both lattice parameters of the non-stoichiometric apatites with exact formulas in the presence of the T-site ions that are not used for training the artificial neural network.
dc.identifier.endpage79
dc.identifier.issn1580-2949
dc.identifier.issue1
dc.identifier.startpage73
dc.identifier.urihttps://hdl.handle.net/11480/4277
dc.identifier.volume48
dc.identifier.wosWOS:000331494300012
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherINST ZA KOVINSKE MATERIALE I IN TEHNOLOGIE
dc.relation.ispartofMATERIALI IN TEHNOLOGIJE
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjecthydroxyapatite
dc.subjectcrystal structure
dc.subjectartificial neural networks
dc.subjectmultilayer-perceptron network
dc.titleARTIFICIAL-NEURAL-NETWORK PREDICTION OF HEXAGONAL LATTICE PARAMETERS FOR NON-STOICHIOMETRIC APATITES
dc.typeArticle

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