Using uncertain prior knowledge to improve identified nonlinear dynamic models
✍ Scribed by Bruno O.S. Teixeira; Luis A. Aguirre
- Book ID
- 104027288
- Publisher
- Elsevier Science
- Year
- 2011
- Tongue
- English
- Weight
- 877 KB
- Volume
- 21
- Category
- Article
- ISSN
- 0959-1524
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✦ Synopsis
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.