Title :
Data reconciliation by two-step risk analysis of modeling
Author :
Congli, Mei ; Guohai, Liu
Author_Institution :
Dept. of Autom., Jiangsu Univ., Zhenjiang
Abstract :
A new method for data reconciliation by risk analysis of modeling is presented in this paper. Yamarura designed an integer programming model for gross error detection and data reconciliation based on Akaike information criterion. But much computational cost is needed for its combinational nature. To reduce computation burden, a new method by two-step risk analysis of modeling is proposed. Measurement modeling risk is analyzed in the first step. Then gross error modeling analyzed based on the minimum measurement modeling risk is considered. The proposed method could effectively reduce the scale of the integer programming problem. Simulation shows the efficiency of the proposed method.
Keywords :
data handling; integer programming; risk analysis; Akaike information criterion; data reconciliation; gross error detection; integer programming model; minimum measurement modeling risk; two-step risk analysis; Automation; Computational efficiency; Computational modeling; Error analysis; Instruments; Iterative methods; Linear programming; Risk analysis; Steady-state; Testing; Akaike information criterion (AIC); Data reconciliation; Gross error; Mixed integer optimization; Risk analysis of modeling;
Conference_Titel :
Control Conference, 2008. CCC 2008. 27th Chinese
Conference_Location :
Kunming
Print_ISBN :
978-7-900719-70-6
Electronic_ISBN :
978-7-900719-70-6
DOI :
10.1109/CHICC.2008.4605669
