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Using uncertain prior knowledge to improve identified nonlinear dynamic models
Ist Teil von
Journal of process control, 2011, Vol.21 (1), p.82-91
Ort / Verlag
Elsevier Ltd
Erscheinungsjahr
2011
Link zum Volltext
Quelle
Elsevier ScienceDirect Journals Complete
Beschreibungen/Notizen
This paper addresses the parameter-estimation problem for linear-in-the-parameter nonlinear models for the case in which uncertain prior knowledge is available in the form of noisy steady-state data. An
uncertainty-weighted least-squares (UWLS) algorithm is developed which takes into account not only the dynamical and the steady-state data but also a measure of relative uncertainty of both data sets. Also, it is shown that a previously developed bi-objective optimization estimator is a special case of UWLS. A consequence of this is that UWLS can take advantage of tools developed in the context of multiobjective optimization to automatically determine an adequate relative uncertainty measure for dynamical and steady-state data sets. The developed algorithm and related ideas are investigated and illustrated by means of examples that use simulated and measured data.