Application of Artificial Neural Networks for modeling drug release from a bicomponent hydrogel system

Artificial Neural Networks (ANNs) have been used as modeling tools for prediction of drug release patterns from bicomponent hydrogel systems based on poly(N-isopropylacrylamide) and sodium alginate. The process modeling was performed using an artificial neural network trained with an evolutionary algorithm, the last one having the role of developing the neural model in an optimal form. The ANN was trained with this algorithm using the available experimental data as the training set. The divergence of the root mean squared error (RMSE) between the output and target values of test set was used as stop criterion. The simulation results showed that drug release profiles from the chosen hydrogels can be modeled accurately using ANNs, the model predictions being closely correlated with the experimental data.

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