Starch foam material performance prediction based on a radial basis function artificial neural network trained by bare‐bones particle swarm optimization with an adaptive disturbance factor

A novel model based on a radial basis artificial neural network and bare‐bones particle swarm optimization tuned with adaptive disturbance factor for predicting the performances of starch‐based foam materials was established. The ethylene–vinyl acetate/starch mass ratio, glycerin content, and NaHCO₃ content were used as the input variables, whereas the tensile strength and rebound rate were taken as the output variables of the model. The prediction results show that model predictions were in great accordance with the experimental values. The root mean square error of prediction and the correlation coefficients were 0.0256 and 0.9873; this indicated the good performance of the model. The model predicted that the tensile strength of the starch‐based foam materials would decrease slowly with increasing glycerin content and showed a V‐shaped variation with increasing NaHCO₃ content. The rebound rate increased with increasing glycerin content and presented an inverted V‐shaped variation with increasing NaHCO₃ content. The predicted results were consistent with the experimental results. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 44252.