Learning a data-dependent kernel function for KPCA-based nonlinear process monitoring

Chemical Engineering Research and Design

Volumn 87, Issue 11, 2009, Pages 1471-1480

  Shao, Ji Dong ^a   Rong, Gang ^a   Lee, Jong Min ^b  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Data dependent kernels; Fault detection; Kernel principal component analysis (KPCA); Learning kernel functions; Nonlinear process monitoring

Indexed keywords

EXISTING METHOD; FEATURE SPACE; GAUSSIAN KERNELS; INPUT DATAS; KERNEL FUNCTION; KERNEL PRINCIPAL COMPONENT ANALYSIS; KERNEL SELECTION; LEARNING KERNEL FUNCTIONS; LEARNING KERNELS; LINEAR PRINCIPAL COMPONENT; MANIFOLD LEARNING; MAXIMUM VARIANCE; NONLINEAR PROCESS; NONLINEAR PROCESS MONITORING; NONLINEAR STRUCTURE; NORMAL OPERATING CONDITIONS; PRINCIPAL COMPONENTS; SIMULATION RESULT; TENNESSEE EASTMAN;

FAULT DETECTION; NONLINEAR ANALYSIS; NONLINEAR SYSTEMS; OPTIMIZATION; PROCESS CONTROL; PROCESS MONITORING;

PRINCIPAL COMPONENT ANALYSIS;

EID: 74249089666     PISSN: 02638762     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cherd.2009.04.011     Document Type: Article

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