A support vector regression approach for recursive simultaneous data reconciliation and gross error detection in nonlinear dynamical systems

Zidonghua Xuebao/ Acta Automatica Sinica

Volumn 35, Issue 6, 2009, Pages 707-716

  Miao, Yu ^a   Su, Hong Ye ^a   Chu, Jian ^a  

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

Author keywords

Data reconciliation; Gross error detection; Nonlinear estimation; Support vector regression

Indexed keywords

CHEMICAL PROCESS; DATA QUALITY; DATA RECONCILIATION; EMPIRICAL RISKS; GROSS ERROR DETECTION; GROSS ERRORS; MODEL COMPLEXITY; NON-LINEAR DYNAMIC SYSTEMS; NON-LINEAR DYNAMICS; NONLINEAR DYNAMIC DATA RECONCILIATION; NONLINEAR ESTIMATION; ONLINE OPTIMIZATION; PERFORMANCE MONITORING; PROCESS DATA; REAL-TIME ESTIMATION; SUPPORT VECTOR; SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSIONS;

CELLULAR RADIO SYSTEMS; CHEMICAL PLANTS; CONTROL THEORY; DYNAMIC PROGRAMMING; DYNAMICAL SYSTEMS; ESTIMATION; EXTENDED KALMAN FILTERS; INDUSTRIAL APPLICATIONS; MAXIMUM LIKELIHOOD ESTIMATION; NONLINEAR ANALYSIS; NONLINEAR DYNAMICAL SYSTEMS; RANDOM ERRORS; REAL TIME SYSTEMS; RECURSIVE FUNCTIONS; REGRESSION ANALYSIS; RISK PERCEPTION; VECTORS;

ERROR DETECTION;

EID: 67650553654     PISSN: 02544156     EISSN: None     Source Type: Journal    
DOI: 10.3724/SP.J.1004.2009.00707     Document Type: Article

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