Prediction of normalized polarity parameter in binary mixed solvent systems using artificial neural networks

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction of normalized polarity parameter ( ) in binary mixed solvent systems at various temperatures and compositions. A three-layered feed forward ANN with back-propagation of error was generated using five parameters as inputs and its output is . It was found that a properly selected and trained neural network could fairly represent the dependence of normalized polarity parameter on temperature and composition. For the evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction of the values in the prediction set, which were not used in the modeling procedure. Correlation coefficient (R) and root mean square error for prediction set are 0.9961 and 0.01187, respectively. The maximum value of IPD (individual percent deviation) for values in the prediction set is 5.116%. The results show non-linear dependence of to temperature and composition in binary mixed solvent systems is significant.