Paper Title:
Robust Data Reconciliation in Linear Process
  Abstract

Data reconciliation is based on spatial redundancy to adjust process data to improve the quality of measurement corruption due to measurement noise. However, the presence of gross errors can severely bias the reconciled results. Robust estimators can significantly reduce the effect of gross errors and yield less biased results. In this paper, a method is proposed to solve the robust data reconciliation problem. By using the proposed method, the robust estimator problem can be transformed into least squares estimator problem which leads to the convenience in computation. Simulation results for a linear process verify the efficiency of the proposed method.

  Info
Periodical
Advanced Materials Research (Volumes 383-390)
Chapter
Chapter 1: Computer-Aided Manufacturing
Edited by
Wu Fan
Pages
667-671
DOI
10.4028/www.scientific.net/AMR.383-390.667
Citation
L. K. Zhou, "Robust Data Reconciliation in Linear Process", Advanced Materials Research, Vols. 383-390, pp. 667-671, 2012
Online since
November 2011
Authors
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Price
$32.00
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