  The viscosity of lead-containing glasses is of fundamental importance for the
manufacturing process, and can be predicted by algorithms such as artificial
neural networks. The SciGlass database was used to provide training, validation
and test data of chemical composition, temperature and viscosity for the
construction of artificial neural networks with node variation in the hidden
layer. The best model built with training data and validation data was compared
with 7 other models from the literature, demonstrating better statistical
evaluations of mean absolute error and coefficient of determination to the test
data, with subsequent sensitivity analysis in agreement with the literature.
Skewness and kurtosis were calculated and there is a good correlation between
the values predicted by the best neural network built with the test data.
