Modeling of magnetic properties of nanocrystalline La-doped barium hexaferrite

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.