On-line principal component analysis with application to process modeling

Neurocomputing

Volumn 82, Issue , 2012, Pages 167-178

  Tang, Jian ^a   Yu, Wen ^b   Chai, Tianyou ^a,c   Zhao, Lijie ^a,d  

^a STATE KEY LABORATORY OF SYNTHETICAL AUTOMATION FOR PROCESS INDUSTRIES   (China)

^b DEPARTAMENTO DE FÍSICA   (Mexico)

^c NORTHEASTERN UNIVERSITY   (China)

^d SHENYANG UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

Author keywords

On line modeling; Principal component analysis; Process modeling; Recursive algorithm

Indexed keywords

ADAPTIVE ABILITY; BENCH-MARK PROBLEMS; EXPERIMENTAL DATA; IN-PROCESS MONITORING; INDUSTRIAL PROCESSS; LINEAR DEPENDENCE; MODELING ACCURACY; MODELING METHOD; MOVING WINDOW PCA; ONLINE MODELING; PCA ALGORITHMS; PCA MODEL; PRINCIPAL COMPONENTS; PROCESS MODELING; RECURSIVE ALGORITHMS; RECURSIVE PCA; SYNTHETIC DATA; TIME VARYING; TIME-VARYING PROCESS;

ALGORITHMS; BALL MILLS; PROCESS MONITORING; TIME VARYING SYSTEMS;

PRINCIPAL COMPONENT ANALYSIS;

ACCURACY; ALGORITHM; ANALYTICAL PARAMETERS; APPROXIMATE LINEAR DEPENDENCE; ARTICLE; INFORMATION PROCESSING; MATHEMATICAL COMPUTING; ONLINE SYSTEM; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROCESS MODEL; SAMPLING; STATISTICAL ANALYSIS;

EID: 84856491836     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.10.026     Document Type: Article

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