Melt index prediction of polypropylene based on stacked neural networks-ridge regression (SNNs-RR) was studied. Single neural network model generalization capability could be significantly improved by using the stacked neural network model. Proper determination of the stacking weights was essential for good SNNs model performance

the determination of appropriate weights for combining individual networks using ridge regression was proposed. The results of using SNNs-RR model demonstrated significant improvement in model accuracy and robustness

as compared with using the single neural network model.