Bursa Uludağ Üniversitesi Açık Erişim Sistemi
Bursa Uludağ Üniversitesinin araştırma çıktılarının yer aldığı açık erişim sistemidir.
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http://hdl.handle.net/11452/24166| Başlık: | Modeling of magnetic properties of nanocrystalline La-doped barium hexaferrite |
| Yazarlar: | Sözeri, Hüseyin Özkan, Hüsnü Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Anabilim Dalı. Küçük, İlker Semih 6602910810 |
| Anahtar kelimeler: | Physics La doped Barium ferrites Magnetic properties Modeling Neural network Perceptron neural-networks Sol-gel technique High coercivity Ferrite Powder Cores Melt Ammonium compounds Barium Barium compounds Ferrite Ferrites Gyrators Hyperbolic functions Lanthanum alloys Learning algorithms Magnetic fields Magnetic properties Magnetization Sintering Ammonium nitrate melt Applied magnetic fields Artificial neural network Barium ferrites Barium hexaferrites Correlation coefficient Hidden layers Hyperbolic tangent Input datas Input parameter La doped Evenberg-marquardt learning algorithms Modeling Multilayer perceptron neural networks Nanocrystallines Output layer Sigmoid transfer function Sintering temperatures Training sets Neural networks |
| Yayın Tarihi: | May-2011 |
| Yayıncı: | Springer |
| Atıf: | Küçük, İ. vd. (2011). "Modeling of magnetic properties of nanocrystalline La-doped barium hexaferrite". Journal of Superconductivity and Novel Magnetism, 24(4), 1333-1337. |
| Özet: | In this paper an artificial neural network (ANN) has been developed to compute the magnetization of the pure and La-doped barium ferrite powders synthesized in ammonium nitrate melt. The input parameters were: the Fe/Ba ratio, La content, sintering temperature, HCl washing and applied magnetic field. A total of 8284 input data set from currently measured 35 different samples with different Fe/Ba ratios, La contents and washed or not washed in HCl were available. These data were used in the training set for the multilayer perceptron (MLP) neural network trained by Levenberg-Marquardt learning algorithm. The hyperbolic tangent and sigmoid transfer functions were used in the hidden layer and output layer, respectively. The correlation coefficients for the magnetization were found to be 0.9999 after the network was trained. |
| URI: | https://doi.org/10.1007/s10948-010-0828-3 https://link.springer.com/article/10.1007/s10948-010-0828-3 http://hdl.handle.net/11452/24166 |
| ISSN: | 1557-1939 |
| Koleksiyonlarda Görünür: | Scopus Web of Science |
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