When identifying a dynamic system the model has to be validated as well. For an errors-in-variables situation where both input and output measurements are noise corrupted, this is a nontrivial task, seldom treated in the literature. Some different approaches for model validation are introduced and evaluated by theoretical analysis as well as application to simulated data.
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