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

Kockan U., Ozturk F., EVİS Z.

MATERIALI IN TEHNOLOGIJE, cilt.48, sa.1, ss.73-79, 2014 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 48 Sayı: 1
  • Basım Tarihi: 2014
  • Dergi Adı: MATERIALI IN TEHNOLOGIJE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.73-79
  • Anahtar Kelimeler: hydroxyapatite, crystal structure, artificial neural networks, multilayer-perceptron network, MATERIALS SCIENCE, HYDROXYAPATITE, COMPOSITES, CATIONS, BONE
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

In 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.