Statistical Analysis of Nonlinear Processes Based on Penalty Factor
Copyright policy )Statistical Analysis of Nonlinear Processes Based on Penalty Factor
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.
- Northwestern University United States
- North University of China China (People's Republic of)
- Northeastern University China (People's Republic of)
- State Key Laboratory of Synthetical Automation for Process Industries China (People's Republic of)
Applications of statistics in engineering and industry; control charts, Factor analysis and principal components; correspondence analysis
Applications of statistics in engineering and industry; control charts, Factor analysis and principal components; correspondence analysis
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!