Using dissolution test data as inputs for ANN designed by Visual Basic programming language, the ideal number of neurons in the hidden layer was determined trial and error methodology to guarantee the best performance of constructed ANN.



Visual Basic
In this work, Artificial Neural Networks (ANNs) were used to optimize melatonin release from hydrophilic polymer matrices.  Using dissolution test data as inputs for ANN designed by Visual Basic programming language, the ideal number of neurons in the hidden layer was determined trial and error methodology to guarantee the best performance of constructed ANN.


 Results showed that the ANN with nine neurons in the hidden layer had the best results.  ANN was examined to check its predictability and then used to determine the best formula that can mimic the release of melatonin from a marketed brand using similarity fit factor.


 This work shows the possibility of using ANN to optimize the composition of prolonged-release melatonin tablets having dissolution profile desired.
ANN