Standard identification methods give biased parameter estimates when the recorded signals are corrupted by noise on both input and output sides. When the system is close to be non-identifiable, the bias can be large. The paper discusses the possibilities and potential benefits when using either a reduced model structure or a full errors-in-variables model. The case of using an instrumental variable estimator is also treated.
Söderström, T., Soverini, U. (2023). Aspects on errors-in-variables identification: Some ways to mitigate a large bias [10.1016/j.ifacol.2023.10.1383].
Aspects on errors-in-variables identification: Some ways to mitigate a large bias
Soverini, Umberto
2023
Abstract
Standard identification methods give biased parameter estimates when the recorded signals are corrupted by noise on both input and output sides. When the system is close to be non-identifiable, the bias can be large. The paper discusses the possibilities and potential benefits when using either a reduced model structure or a full errors-in-variables model. The case of using an instrumental variable estimator is also treated.File in questo prodotto:
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