Prediction of the pH and the temperature-dependent swelling behavior of Ca2+Ca2+-alginate hydrogels by artificial neural networks

This paper considers the possibility of using artificial neural network models to identify model for swelling behavior as new techniques. Multi-layer feed-forward, radial basis function and generalized regression neural network models were employed to predict the swelling behaviors of Ca2+Ca2+-alginate hydrogels under different environmental conditions of pH and temperature. The results show that an excellent correlation between the experimental and predicted swelling ratios was obtained by the artificial neural networks. Generalized regression neural network has a better performance than the other neural network models. The absolute mean error, the determination coefficient and the standard error of prediction were used as performance criteria. In addition, the performances of the neural network models are significantly superior compared with those of second-order swelling kinetics, quadratic and cubic models of response surface methodology.