Estimation of state variables in semiautogenous mills by means of a neural moving horizon state estimator

被引：0

作者：

Carvajal, Karina ^{[1
]}

Acuna, Gonzalo ^{[2
]}

机构：

[1] Univ Atacama, Fac Ingn, Copayapu 485, Copiapo, Chile

[2] Univ Santiago Chile, Fac Ingn, Santiago, Chile

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2007, PT 1, PROCEEDINGS | 2007年 / 4491卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1255 / +

页数：3

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