Using an artificial neural network for the evaluation of the parameters controlling PVA/chitosan electrospun nanofibers diameter

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Abstract
The purpose of this study was to investigate the validity of an artificial neural network (ANN) method in the prediction of nanofiber diameter to assess the parameters involved in controlling fiber form and thickness. A mixture of polymers including poly(vinyl alcohol) (PVA) and chitosan (CS) at different ratios was chosen as the nanofiber base material. The various samples of nanofibers were fabricated as training and testing datasets for ANN modeling. Different networks of ANN were designed to achieve the purposes of this study. The best network had three hidden layers with 8, 16 and 5 nodes in each layer, respectively. The mean squared error and correlation coefficient between the observed and the predicted diameter of the fibers in the selected model were equal to 0.09008 and 0.93866, respectively, proving the efficacy of the ANN technique in the prediction process. Finally, three-dimensional graphs of the electrospinning parameters involved and nanofiber diameter were plotted to scrutinize the implications.

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