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  • Artificial-neural-network prediction of hexagonal lattice parameters for non-stoichiometric apatites = Napovedovanje heksagonalnih mrežnih parametrov z umetno nevronsko mrežo
    Kockan, Umit ; Ozturk, Fahrettin ; Evis, Zafer
    In this study, hexagonal lattice parameters (a and c) and unit-cell volumes of non-stoichiometric apatites of M10(TO4)6X2 are predicted from their ionic radii with artificial neural networks. A ... multilayer-perceptron network is usedfor 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.
    Vir: Materiali in tehnologije = Materials and technology. - ISSN 1580-2949 (Letn. 48, št. 1, jan.-feb. 2014, str. 73-79)
    Vrsta gradiva - članek, sestavni del ; neleposlovje za odrasle
    Leto - 2014
    Jezik - angleški
    COBISS.SI-ID - 1031850

    Povezava(-e):

    http://mit.imt.si/Revija/
    Digitalna knjižnica Slovenije - dLib.si

vir: Materiali in tehnologije = Materials and technology. - ISSN 1580-2949 (Letn. 48, št. 1, jan.-feb. 2014, str. 73-79)

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