Assessment of Neural Network training algorithms for the prediction of Polymeric Inclusion Membranes Efficiency

Abstract

The aim of this study is to introduce, through an appropriate selection of the training algorithm, a better and optimum artificial neural network (ANN) that will capable to predict Polymeric Inclusion Membranes (PIMs) Cr(VI) removal efficiency from aqueous solutions. To accomplish that, three training algorithms including Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) have been assessed by training different ANN. The performances of developed models are evaluated by Coefficient of Regression (R 2) and Root Mean Square Error (RMSE) to find the best ANN training algorithms. This study clears that right choice of the training algorithm grants maximizing the predictive capability of the ANN models.