Modeling of Magnetic Properties of Nanocrystalline La-doped Barium Hexaferrite

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Abstract

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.

Keywords

La doped Barium ferrites Magnetic properties Modeling Neural network 
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References

  1. 1.
    Liu, X., Wang, J., Gan, L.M., Ng, S.C., Ding, J.: J. Magn. Magn. Mater. 184, 344 (1998) ADSCrossRefGoogle Scholar
  2. 2.
    Liu, X., Wang, J., Gan, L.M., Ng, S.C.: J. Magn. Magn. Mater. 195, 452 (1999) ADSCrossRefGoogle Scholar
  3. 3.
    Rezlescu, L., Rezlescu, E., Popar, P.D., Rezlescu, N.: J. Magn. Magn. Mater. 193, 288 (1993) ADSCrossRefGoogle Scholar
  4. 4.
    Chin, T.S., Hsu, S.L., Deng, M.C.: J. Magn. Magn. Mater. 120, 64 (1993) ADSCrossRefGoogle Scholar
  5. 5.
    Pramanik, N.C., Fujii, T., Nakanishi, M., Takada, J.: Mater. Lett. 59, 468 (2005) CrossRefGoogle Scholar
  6. 6.
    Zahi, S., Hashim, M., Daud, A.R.: J. Magn. Magn. Mater. 308, 177 (2007) ADSCrossRefGoogle Scholar
  7. 7.
    Sugimoto, S., Haga, K., Kagotani, T., Inomata, K.: J. Magn. Magn. Mater. 290, 1188 (2005) ADSCrossRefGoogle Scholar
  8. 8.
    Yamamoto, S., Hirata, K., Kurisu, H., Matsuura, M., Doi, T., Tamari, K.: J. Magn. Magn. Mater. 235, 342 (2001) ADSCrossRefGoogle Scholar
  9. 9.
    Sivakumar, M., Gedanken, A., Zhong, W., Du, Y.W., Bhattacharya, D., Yeshurun, Y., Felner, I.: J. Magn. Magn. Mater. 268, 95 (2004) ADSCrossRefGoogle Scholar
  10. 10.
    Tartaj, P., Morales, M.P., Gonzalez-Carreno, T., Veintemillas-Verdaguer, S., Serna, C.J.: J. Magn. Magn. Mater. 290, 28 (2005) ADSCrossRefGoogle Scholar
  11. 11.
    Yan, S., Yin, J., Zhou, E.: Colloids Surf. A 287, 153 (2006) CrossRefGoogle Scholar
  12. 12.
    Sözeri, H.: J. Magn. Magn. Mater. 321, 2717 (2009) ADSCrossRefGoogle Scholar
  13. 13.
    Sözeri, H., Özkan, H., Ghazanfari, N., J. Alloys Comp. 428(1–2), 1 (2007) CrossRefGoogle Scholar
  14. 14.
    Haykin, S.: Neural networks: a comprehensive foundation. Macmillan, New York (1994) MATHGoogle Scholar
  15. 15.
    Kucuk, I.: J. Magn. Magn. Mater. 305, 423 (2006) ADSCrossRefGoogle Scholar
  16. 16.
    Kucuk, I.: J. Magn. Magn. Mater. 307, 53 (2006) ADSCrossRefGoogle Scholar
  17. 17.
    Kucuk, N.: Ann. Nucl. Energy 35, 1787 (2008) CrossRefGoogle Scholar
  18. 18.
    Kucuk, I., Sözeri, H., Ozkan, H.: Improvement in coercivity and saturation magnetization of nanocrystalline BaFe12O19 with La doping (under review) Google Scholar
  19. 19.
    Wang, F., Devabhaktuni, V.K., Xi, C., Zhang, Q.: Int. J. RF Microw. Comput. Aided Eng. 9, 216 (1999) CrossRefGoogle Scholar
  20. 20.
    Ubeyli, E.D., Guler, I.: Neurocomputing 62, 349 (2004) CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Physics Department, Faculty of Arts and SciencesUludag UniversityBursaTurkey
  2. 2.National Metrology InstituteTUBITAK-UMEGebze-KocaeliTurkey
  3. 3.Department of PhysicsMiddle East Technical UniversityAnkaraTurkey

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