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In a strict sense, model validation concerns with predictive ability which, to avoid subjectivity, could be assessed only with newly arrived data.
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A further objective is exploring the behavior of these different Bayesian methods for model validation concerning the coherence of their preference for a certain candidate model.
This paper is concerned with model validation for industrial model predictive control systems.
The results concerning the model validation are illustrated and they fully confirm the effectiveness of the proposed model.
This paper is concerned with dynamic model validation through detection of parameter changes using the local detection approach.
Figure 8 gives the DPD model validation as far as the AM AM characteristics are concerned.
Whereas static model validation is well developed, dynamic models remain difficult to satisfactorily validate especially when it concerns large scale systems common in chemical engineering.
Model validation was carried out on the basis of experimental data concerning case accelerations and force reactions.
This framework has been applied on a real plant of West Greece with success in all realistic tests concerning either the control operation or the model validation.
Current model validation methodology is confined to linear models and is primarily concerned with (i) correcting inaccurate model parameters and (ii) ensuring that sufficient elements are included for these cases, using measured data.
In this article, we present the key functionalities of the toolbox, quantify their performance, and discuss the novel implementation of important methodological concepts concerning parameter identifiability, confidence intervals and statistical tests for model validation and discrimination.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com