Abstract
In this study, artificial neural networks were employed to predict thermal conductivity of polyacrylonitrile (PAN) electrospun nanocomposite fibers embedded with multiwalled carbon nanotubes (MWCNTs) and Nickel Zinc ferrites [(Ni0.6Zn0.4) Fe2O4]. The prey predator algorithm was used to train the neural networks to find the best models. This is the first paper on the application of multilayer perception neural network (MLPNN) with the prey predator algorithm for the prediction of thermal conductivity of PAN Electrospun nanocomposites. The method of nonlinear regression was used to minimize the error distribution between the experimental data and the predicted results. Both MWCNTs and Nickel Zinc ferrites were used in different weight proportions. The predicted ANN responses were analyzed statistically using z-test and error functions for both nanoinclusions. The predicted ANN responses for PAN Electrospun nanocomposite fibers were compared with the experimental data and were found in good agreement. (C) 2018 Production and hosting by Elsevier B.V. on behalf of King Saud University.