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Mathematical Problems in Engineering
Volume 2014, Article ID 945948, 9 pages
Research Article

Statistical Analysis of Nonlinear Processes Based on Penalty Factor

State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Liaoning 100819, China

Received 25 July 2014; Accepted 17 August 2014; Published 30 September 2014

Academic Editor: Ligang Wu

Copyright © 2014 Yingwei Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A new process monitoring approach is proposed for handling the nonlinear monitoring problem in the electrofused magnesia furnace (EFMF). Compared to conventional method, the contributions are as follows: (1) a new kernel principal component analysis is proposed based on loss function in the feature space; (2) the model of kernel principal component analysis based on forgetting factor is updated; (3) a new iterative kernel principal component analysis algorithm is proposed based on penalty factor.