Abstract
In this work, the prediction of heavy petroleum fractions was significantly improved by using a backpropagation neural network model. It was found that scaling the data, fed to the neural net, improved the convergence of the estimated parameter (viscosity) in reasonable time with acceptable accuracy. An absolute error of 3.4% was achieved which is found to be better than those by other conventional methods.
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