期刊
NEUROCOMPUTING
卷 82, 期 -, 页码 167-178出版社
ELSEVIER
DOI: 10.1016/j.neucom.2011.10.026
关键词
Principal component analysis; Recursive algorithm; On-line modeling; Process modeling
资金
- Natural Science Foundation of China [61020106003, 60874057]
- National Science Foundation for Post-doctoral Scientists of China [20100471464]
Principal component analysis (PCA) has been widely applied in process monitoring and modeling. The time-varying property of industrial processes requires the adaptive ability of the PCA. This paper introduces a novel PCA algorithm, named on-line PCA (OLPCA). It updates the PCA model according to the process status. The approximate linear dependence (ALD) condition is used to check each new sample. A recursive algorithm is proposed to reconstruct the PCA model with selected samples. Three types of experiments, a synthetic data, a benchmark problem, and a ball mill load experimental data, are used to illustrate our modeling method. The results show that the proposed OLPCA is computationally faster, and the modeling accuracy is higher than conventional moving window PCA (MWPCA) and recursive PCA (RPCA) for time-varying process modeling. (c) 2011 Elsevier By. All rights reserved.
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