Abstract
A method of moving horizon state estimation (MHSE) including a recurrent neural network as the dynamic model is used as an estimator of the filling level of the mill for a semiautogenous ore grinding process. The results are compared to those of a simple neural network acting as an estimator. They show the advantages of the Neural-MHSE, especially concerning robustness under large perturbations of the state variables (index of agreement > 0.9), which would favor its application to industrial scale processes. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Carvajal, K., & Acuña, G. (2007). Estimation of state variables in semiautogenous mills by means of a neural moving horizon state estimator. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4491 LNCS, pp. 1255–1264). Springer Verlag. https://doi.org/10.1007/978-3-540-72383-7_146
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
