Nonlinear process monitoring using improved kernel principal component analysis

Kernel principal component analysis (KPCA) has become a popular technique for process monitoring in recent years. However, the performance largely depends on kernel function, yet methods to choose an appropriate kernel function among infinite ones have only been sporadically touched in the research...

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Bibliographic Details
Published in:	Chinese Control and Decision Conference pp. 5832 - 5843
Main Authors:	Wei, Chihang, Chen, Junghui, Song, Zhihuan
Format:	Conference Proceeding Journal Article
Language:	English
Published:	IEEE 01.05.2016
Subjects:	Approximation Eigenvalues and eigenfunctions Fault Detection Kernel Kernel Function Approximation Kernel functions Kernels Learning Linear programming Manifold Learning Manifolds Mathematical models Monitoring Nonlinear Process Monitoring Principal component analysis Training
ISSN:	1948-9447
Online Access:	Get full text
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