DocumentCode :
550375
Title :
Modified recursive partial least squares algorithm with application to modeling parameters of ball mill load
Author :
Tang Jian ; Zhao Lijie ; Yu Wen ; Chai Tianyou ; Yue Heng
Author_Institution :
Key Lab. of Integrated Autom. of Process Ind., Northeastern Univ., Shenyang, China
fYear :
2011
fDate :
22-24 July 2011
Firstpage :
5277
Lastpage :
5282
Abstract :
Recursive partial least squares (RPLS) regression is effectively used in process monitoring and modeling to deal with the stronger collinearity of the process variables and slow time-varying property of industrial processes. Aim at the RPLS cannot solve the modeling speed and the accuracy problems effectively, a modified sample-wise RPLS algorithm is proposed in this paper. It updates the PLS model according to the process status. We use the approximate linear dependence (ALD) condition to check each new sample. The model is reconstructed recursively such that the new samples satisfy the ALD condition. Experimental study on modeling parameters of ball mill load shows that the proposed modified RPLS algorithm is computationally faster, and the modeling accuracy is higher than conventional RPLS for the time-varying process.
Keywords :
ball milling; parameter estimation; process monitoring; regression analysis; time-varying systems; approximate linear dependence condition; ball mill load; industrial processes; parameter modeling; process modeling; process monitoring; recursive partial least squares regression; sample-wise RPLS algorithm; time-varying process; Adaptation models; Computational modeling; Data models; Dictionaries; Load modeling; Predictive models; Training; Approximate linear dependence; Mill load; On-line modeling; Recursive partial least squares;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2011 30th Chinese
Conference_Location :
Yantai
ISSN :
1934-1768
Print_ISBN :
978-1-4577-0677-6
Electronic_ISBN :
1934-1768
Type :
conf
Filename :
6000713
Link To Document :
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