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 < 1.0), which would favor its application to industrial scale processes.
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Acuña, G., Curilem, M. (2009). Comparison of Neural Networks and Support Vector Machine Dynamic Models for State Estimation in Semiautogenous Mills. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds) MICAI 2009: Advances in Artificial Intelligence. MICAI 2009. Lecture Notes in Computer Science(), vol 5845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05258-3_42
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DOI: https://doi.org/10.1007/978-3-642-05258-3_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05257-6
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