Incremental Parameter Estimation under Rank-Deficient Measurement Conditions
AbstractThe computation and modeling of extents has been proposed to handle the complexity of large-scale model identification tasks. Unfortunately, the existing extent-based framework only applies when certain conditions apply. Most typically, it is required that a unique value for each extent can be computed. This severely limits the applicability of this approach. In this work, we propose a novel procedure for parameter estimation inspired by the existing extent-based framework. A key difference with prior work is that the proposed procedure combines structural observability labeling, matrix factorization, and graph-based system partitioning to split the original model parameter estimation problem into parameter estimation problems with the least number of parameters. The value of the proposed method is demonstrated with an extensive simulation study and a study based on a historical data set collected to characterize the isomerization of
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Villez, K.; Billeter, J.; Bonvin, D. Incremental Parameter Estimation under Rank-Deficient Measurement Conditions. Processes 2019, 7, 75.
Villez K, Billeter J, Bonvin D. Incremental Parameter Estimation under Rank-Deficient Measurement Conditions. Processes. 2019; 7(2):75.Chicago/Turabian Style
Villez, Kris; Billeter, Julien; Bonvin, Dominique. 2019. "Incremental Parameter Estimation under Rank-Deficient Measurement Conditions." Processes 7, no. 2: 75.
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