Journal
AICHE JOURNAL
Volume 62, Issue 12, Pages 4334-4345Publisher
WILEY
DOI: 10.1002/aic.15347
Keywords
block adaptive Kernel principal component analysis; online monitoring; Gram matrix; iterative algorithm approach
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Funding
- National High Technology Research and Development Program of China (863 Program) [2014AA041803]
- Open Project of State Key Laboratory of Industrial Control Technology of Zhejiang University [ICT1600193]
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On-line modeling of multivariate nonlinear system based on multivariate statistical methods has been studied extensively due to its industrial requirements. In order to further improve the modeling efficiency, a fast Block Adaptive Kernel Principal Component Analysis algorithm is proposed. Comparing with the existing work, the proposed algorithm (1) does not rely on iterative computation in the calculating process, (2) combines the up- and downdating operations to become a single one (3) and describes the adaptation of the Gram matrix as a series of rank-1 modification. In addition, (4) the updation of the eigenvalues and eigenvectors is of O(N) and high-precision. The computational complexity analysis and the numerical study show that the derived strategy possesses better ability to model the time-varying nonlinear variable interrelationships in process monitoring. (c) 2016 American Institute of Chemical Engineers AIChE J, 62: 4334-4345, 2016
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