Comparison of Neural Networks and Support Vector Machine Dynamic Models for State Estimation in Semiautogenous Mills

Authors:
Gonzalo Acuña;Millaray Curilem
Affiliations:
Depto. de Ingeniería Informática, Universidad de Santiago de Chile, USACH, Santiago, Chile 3659;Dpto. De Ingeniería Eléctrica, Universidad de La Frontera, UFRO, Temuco, Chile 01145
Venue:
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Year:
2009

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Abstract

Development of performant state estimators for industrial processes like copper extraction is a hard and relevant task because of the difficulties to directly measure those variables on-line. In this paper a comparison between a dynamic NARX-type neural network model and a support vector machine (SVM) model with external recurrences for estimating the filling level of the mill for a semiautogenous ore grinding process is performed. The results show the advantages of SVM modeling, especially concerning Model Predictive Output estimations of the state variable (MSE