Modeling the Impedance Behavior of Ionic Conductors (AgPO3)1-x(Ag2SO4)x Glass System Using Artificial Neural Network

The dielectric permittivity and conductivity of (AgPO3)1-x(Ag2SO4)x compound was investigated at different concentrations of (Ag2SO4). The effect of concentration on AC conductivity and permittivity as well as temperature and frequency was investigated in order to model this behavior. Multidimensional mathematical models were as proposed to predict the impedance components and the dielectric permittivity components of the glass system as a function of temperatures, frequencies and concentrations. Artificial Neural Network (ANN) and nonlinear regression approaches were set as curve fitting techniques in order to construct models based on 1700 points of data. This model can be then used to predict these proprieties at any concentration and therefore helping the product designer to choose the proper mixing and temperature conditions. For ANN, 20, 50, and 100 nodes in a single hidden layer neural network were considered. Although data results of the two approaches showed a good alignment with experimental data, the ANN model with twenty nodes was able to predict the outputs with lower MSE values range from 0.008 to 0.012 for impedance and from 0.006 to 0.008 for dielectric losses than the regression technique. Moreover, R2 values for the neural network were over 99% in both training and testing of impedance and dielectric permittivity while R2 values for non-linear regression vary between 73.86% to 94.75%. The proposed ANN model can be of a great help to find the optimal dielectric permittivity and conductivity of (AgPO3)1-x(Ag2SO4)x compound when dealing with a specific application.