Abstract
The dielectric permittivity and conductivity of (AgPO<sub>3</sub>)<sub>1-x</sub>(Ag<sub>2</sub>SO<sub>4</sub>)<sub>x</sub> compound was investigated at different concentrations of (Ag<sub>2</sub>SO<sub>4</sub>). 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 <img src=image/15115401_01.gif> and the dielectric permittivity components <img src=image/15115401_02.gif> 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, R<sup>2</sup> values for the neural network were over 99% in both training and testing of impedance and dielectric permittivity while R<sup>2</sup> 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 (AgPO<sub>3</sub>)<sub>1-x</sub>(Ag<sub>2</sub>SO<sub>4</sub>)<sub>x</sub> compound when dealing with a specific application.
Highlights
Neural Networks (NNs) have been extensively used in the literature to perform several artificial intelligence functions, such as pattern classification, object classification, data compression and regression
Maximum stress, and stress ratio were considered as input variables. Their Artificial Neural Network (ANN) model was compared with other methods as well as different types of another artificial neural network, such as PCAN, radial basis function (RBF), and modular network (MN)
The annealing temperature was raised to 400 oC and hold for another 2 hours in order to get rid of the byproduct gas
Summary
Neural Networks (NNs) have been extensively used in the literature to perform several artificial intelligence functions, such as pattern classification, object classification, data compression and regression (function approximation). Maximum stress, and stress ratio were considered as input variables Their ANN model was compared with other methods as well as different types of another artificial neural network, such as PCAN , RBF, and MN. Two mathematical approaches (i.e., the linear regression and the computational neural network) were used to predict glass transition temperature for different structures [9]. The effect of shear strain and the orientation angle of carbon fibers and glass fibers on shear stress was studied by Bezzera et al [12] They found that the best mathematical model of predicting non-linear relation between stress and strain is ANN. The recorded data was utilized to build different mathematical neural network models and compared them to regression models
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
