On the regularization of dynamic data reconciliation problems

By T. Binder, Luise Blank, Wolfgang Dahmen, Wolfgang Marquardt. 2002

46 citations recorded by the source corpus.

Abstract Dynamic data reconciliation problems are discussed from the perspective of the mathematical theory of ill-posed inverse problems. Regularization is of crucial importance to obtain satisfactory estimation quality of the reconciled variables. Usually, some penalty is added to the least-squares objective to achieve a well-posed problem. However, appropriate discretization schemes of the time-continuous problem act themselves as regularization, reducing the need of problem modification. Based on this property, we suggest to refine successively the discretization of the continuous problem starting from a coarse grid, to find a suitable regularization which renders a good compromise between (measurement) data and regularization error in the estimate. In particular, our experience supports the conjecture, that non-equidistant discretization grids offer advantages over uniform grids.

Subfields: Biomedical Engineering, Computational Mechanics, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/bdc2deeb43aeefcec85ec0b6645277498905fff3