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Estimation of State Variables in Semiautogenous Mills by Means of a Neural Moving Horizon State Estimator
| dc.contributor.author | Carvajal, Karina | |
| dc.contributor.author | Gonzalo Acuña | |
| dc.date.accessioned | 2020-09-14T15:23:54Z | |
| dc.date.available | 2020-09-14T15:23:54Z | |
| dc.date.issued | 2007 | |
| dc.identifier.citation | International Symposium on Neural Networks, 2007. Part of the Lecture Notes in Computer Science book series (LNCS, volume 4491) | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12740/15730 | |
| dc.description.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. | |
| dc.rights | Atribución-SinDerivadas 3.0 Chile (CC BY-ND 3.0 CL) | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nd/3.0/cl/ | |
| dc.source.uri | http://link.springer.com/chapter/10.1007%2F978-3-540-72383-7_146 | |
| dc.subject | ROOT MEAN SQUARE | |
| dc.subject | RECURRENT NEURAL NETWORK | |
| dc.subject | RESIDUAL STANDARD DEVIATION | |
| dc.subject | SOFTWARE SENSOR | |
| dc.subject | FILLING LEVEL | |
| dc.subject.other | RED NEURONAL RECURRENTE | |
| dc.subject.other | MEDIA CUADRÁTICA | |
| dc.title | Estimation of State Variables in Semiautogenous Mills by Means of a Neural Moving Horizon State Estimator | |
| dc.type | Artículo de Revista | |
| dc.relation.vrip | http://link.springer.com/chapter/10.1007%2F978-3-540-72383-7_146 | |
| dc.unidad | Informática | |
| dc.database | Scopus |
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Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución-SinDerivadas 3.0 Chile (CC BY-ND 3.0 CL)