DocumentCode :
2339549
Title :
Predictive Models of Aluminum Reduction Cell Based on LS-SVM
Author :
Yan, Gang ; Liang, Ximing
Author_Institution :
Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China
Volume :
2
fYear :
2010
fDate :
18-20 Dec. 2010
Firstpage :
99
Lastpage :
102
Abstract :
Bath temperature and alumina concentration are two important but hard to measure online parameters of aluminum reduction cell. To this problem, a novel method based on least squares support vector machine (LS-SVM) and chaos optimization is proposed to establish predictive models of the two parameters. This method employs chaos optimization technique to iterate and search in feasible regions so as to find optimal LS-SVM algorithm parameters and corresponding model parameters. The simulation results show that this method has smaller absolute error and relative error than those of neural network method.
Keywords :
alumina; aluminium manufacture; chaos; error statistics; least squares approximations; neural nets; optimisation; production engineering computing; support vector machines; LS-SVM; alumina; aluminum reduction cell predictive models; bath temperature; chaos optimization technique; least squares support vector machine; neural network method; alumina concentration; aluminum reduction cell; bath temperature; chaos optimization; least squares support vector machine; predictive model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Manufacturing and Automation (ICDMA), 2010 International Conference on
Conference_Location :
ChangSha
Print_ISBN :
978-0-7695-4286-7
Type :
conf
DOI :
10.1109/ICDMA.2010.12
Filename :
5701358
Link To Document :
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