On the regularization of dynamic data reconciliation problems

Regularization of Dynamic Data Reconciliation Problems by Projection 1

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 well{posedness of the problem. However, appropriate discretization schemes of the t...

a study on insurer solvency by panel data model: the case of iranian insurance market

the aim of this thesis is an approach for assessing insurer’s solvency for iranian insurance companies. we use of economic data with both time series and cross-sectional variation, thus by using the panel data model will survey the insurer solvency.

On the Regularization of Dynamic

Dynamic Data Reconciliation for Improved Process Performance

One of main thrusts of modern plant operation is to improve the quality of online information in distributed control system (DCS). Accurate information about the current state of a process is paramount for plant monitoring and control. Unfortunately, process measurements are often corrupted by measurement noise. The presence of measurement noise not only prevents plant operators from identifyin...

Using Sub-models for Dynamic Data Reconciliation

The optimal approach for dynamic data reconciliation consists in using a complete and exact process model. Unfortunately, such a model is difficult to obtain in industrial practice. Through an example, several observers based on static, stationary and dynamic sub-models are designed and compared to the optimal approach. The comparisons illustrate that, for the given conditions, static observers...