• using uncertain prior knowledge to improve identified nonlinear dynamic models

    جزئیات بیشتر مقاله
    • تاریخ ارائه: 1392/01/01
    • تاریخ انتشار در تی پی بین: 1392/01/01
    • تعداد بازدید: 657
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
     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.

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