Adaptive Kernel Principal Component Analysis (KPCA) for Monitoring Small Disturbances of Nonlinear Processes

2010 ◽  
Vol 49 (5) ◽  
pp. 2254-2262 ◽  
Author(s):  
Chun-Yuan Cheng ◽  
Chun-Chin Hsu ◽  
Mu-Chen Chen
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Nonlinear Processes ◽  
Adaptive Kernel ◽  
Small Disturbances

scholarly journals Monitoring of Nonlinear Time-Delay Processes Based on Adaptive Method and Moving Window

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yunpeng Fan ◽  
Wei Zhang ◽  
Yingwei Zhang
Keyword(s):  
Principal Component Analysis ◽  
Time Delay ◽  
Principal Component ◽  
Component Analysis ◽  
Adaptive Method ◽  
Kernel Principal Component Analysis ◽  
Moving Window ◽  
Data Space ◽  
Weighted Principal Component ◽  
Adaptive Kernel

A new adaptive kernel principal component analysis (KPCA) algorithm for monitoring nonlinear time-delay process is proposed. The main contribution of the proposed algorithm is to combine adaptive KPCA with moving window principal component analysis (MWPCA) algorithm, and exponentially weighted principal component analysis (EWPCA) algorithm respectively. The new algorithm prejudges the new available sample with MKPCA method to decide whether the model is updated. Then update the KPCA model using EWKPCA method. And also extend MPCA and EWPCA from linear data space to nonlinear data space effectively. Monitoring experiment is performed using the proposed algorithm. The simulation results show that the proposed method is effective.

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