When recorded signals are corrupted by noise on both input and output sides, all standard identification methods give biased parameter estimates, due to the presence of input noise. This report discusses in what situations such a bias is large and, consequently, when the errors-in-variables identification methods are to be preferred.
Söderström, T., Soverini, U. (2021). When are errors-in-variables aspects particularly important to consider in system identification?. Uppsala : Department of Information Technology, Uppsala University.
When are errors-in-variables aspects particularly important to consider in system identification?
Soverini, UmbertoSecondo
2021
Abstract
When recorded signals are corrupted by noise on both input and output sides, all standard identification methods give biased parameter estimates, due to the presence of input noise. This report discusses in what situations such a bias is large and, consequently, when the errors-in-variables identification methods are to be preferred.File in questo prodotto:
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