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Title: Framework for joint data reconciliation and parameter estimation
Authors: JOE YEN YEN
Keywords: data reconciliation, generalized distribution, generalized T, robust estimation, partially adaptive estimation, estimation efficiency
Issue Date: 23-Dec-2004
Citation: JOE YEN YEN (2004-12-23). Framework for joint data reconciliation and parameter estimation. ScholarBank@NUS Repository.
Abstract: This thesis proposes the use of a generalized distribution, namely the Generalized T (GT) distribution in the joint estimation of process states and model parameters. The desirable properties of the GT-based estimator are its robustness, simplicity, flexibility and efficiency for the wide range of commonly encountered distributions (including Box-Tiao and t-distributions) that belong to the GT distribution family. To achieve estimation efficiency, the parameters of the GT distribution are adapted from the data through preliminary estimation. The strategy is applied to data from both the virtual version and a trial run of a chemical engineering pilot plant. The results confirm the robustness and efficiency of the estimator.
Appears in Collections:Master's Theses (Open)

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