— Standard identification methods give biased parameter estimates when recorded signals are corrupted by noise on both input and output sides. In previous papers it has been shown that the bias is significant in case the system is almost non-identifiable. This situation is investigated here for some general model structures
Söderström, T., Soverini, U. (2024). Bias Considerations When Identifying Systems from Noisy Input-Output Data - Extensions to General Model Structures. IEEE [10.23919/ECC64448.2024.10590941].
Bias Considerations When Identifying Systems from Noisy Input-Output Data - Extensions to General Model Structures
Soverini U.Secondo
2024
Abstract
— Standard identification methods give biased parameter estimates when recorded signals are corrupted by noise on both input and output sides. In previous papers it has been shown that the bias is significant in case the system is almost non-identifiable. This situation is investigated here for some general model structuresFile in questo prodotto:
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