Comparison of neural networks and support vector machine dynamic models for state estimation in semiautogenous mills

Acuna G.; Curilem M.

Keywords: Dynamic systems NARX models Neural networks Semiautogenous mills Support vector regression

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. © 2009 Springer-Verlag Berlin Heidelberg.

Más información

Título según SCOPUS: Comparison of neural networks and support vector machine dynamic models for state estimation in semiautogenous mills
Título de la Revista: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen: 5845 LNAI
Editorial: Springer Verlag
Fecha de publicación: 2009
Página de inicio: 478
Página final: 487
Idioma: eng
DOI:

10.1007/978-3-642-05258-3_42

Notas: SCOPUS