Robust supervised probabilistic principal component analysis model for soft sensing of key process variables

2015 ◽  
Vol 122 ◽  
pp. 573-584 ◽  
Author(s):  
Jinlin Zhu ◽  
Zhiqiang Ge ◽  
Zhihuan Song
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Process Variables ◽  
Analysis Model ◽  
Principal Component Analysis Model ◽  
Soft Sensing ◽  
Probabilistic Principal Component Analysis

Study on the Effect Factors of Non-Perormance Loan Ratio of Chinese Commerical Banks

2011 ◽  
Vol 50-51 ◽  
pp. 728-732
Author(s):  
Ping Li ◽  
Ming Ying Zhuo ◽  
Li Chao Feng ◽  
Rui Zhang
Keyword(s):  
Principal Component Analysis ◽  
Commercial Banks ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Analysis Model ◽  
Principal Component Analysis Model ◽  
Effect Factors ◽  
Financial Indicator ◽  
Commerical Banks

Non-performance loan ratio is one of the important assessment criteria of the security of credit assets. It is also an important financial indicator to evaluate the general strength of commercial banks. Using principal component analysis method and statistical software SPSS16.0 and based on the non-performance loan ratio and relative data of some commercial banks in China in 2007, this paper provided a principal component analysis model for the non-performance loan ratio of China’s commercial banks. The factors that affect the non-performance loan ratio were refined in this paper. Finally, the characteristics of effect factors of each bank were analyzed and compared in detail.

scholarly journals Perceived difficulties and complications in learners of phacoemulsification: A principal component analysis model

2019 ◽  
Vol 67 (2) ◽  
pp. 213 ◽  
Author(s):  
Rohit Saxena ◽  
Sagnik Sen ◽  
Mukesh Patil ◽  
Atul Kumar ◽  
SreelakshmiP Amar ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Model ◽  
Principal Component Analysis Model

scholarly journals Process monitoring based on distributed principal component analysis with angle-relevant variable selection

2019 ◽  
Vol 15 (6) ◽  
pp. 155014771985758 ◽  
Author(s):  
Chen Xu ◽  
Fei Liu
Keyword(s):  
Principal Component Analysis ◽  
Variable Selection ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Process Variables ◽  
Analysis Model ◽  
Relevant Variable ◽  
Selection For ◽  
Monitoring Performance

Multivariate statistics process monitoring can achieve dimensionality reduction and latent feature extraction on process variables. However, process variables without beneficial information may affect the monitoring performance. This article proposes a distributed principal component analysis method based on the angle-relevant variable selection for plant-wide process monitoring. The directions of principal components are utilized to construct the sub-blocks, where the variables in each sub-block are determined by angle. After establishing the principal component analysis model in each sub-block, the monitoring results are fused by Bayesian inference. The simulation results show that the proposed method can select the responsible variables effectively and enhance the monitoring performance.

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