Nonparametric identification of nonlinear dynamic systems
Dissertationsschrift
(Sprache: Englisch)
A nonparametric identification method for highly nonlinear systems is presented that is able to reconstruct the underlying nonlinearities without a priori knowledge of the describing nonlinear functions. The approach is based on nonlinear Kalman Filter...
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A nonparametric identification method for highly nonlinear systems is presented that is able to reconstruct the underlying nonlinearities without a priori knowledge of the describing nonlinear functions. The approach is based on nonlinear Kalman Filter algorithms using the well-known state augmentation technique that turns the filter into a dual state and parameter estimator, of which an extension towards nonparametric identification is proposed in the present work.
Klappentext zu „Nonparametric identification of nonlinear dynamic systems “
A nonparametric identification method for highly nonlinear systems is presented that is able to reconstruct the underlying nonlinearities without a priori knowledge of the describing nonlinear functions. The approach is based on nonlinear Kalman Filter algorithms using the well-known state augmentation technique that turns the filter into a dual state and parameter estimator, of which an extension towards nonparametric identification is proposed in the present work.
Bibliographische Angaben
- Autor: Gábor Kenderi
- 2018, 241 Seiten, mit Abbildungen, Masse: 17 x 24 cm, Kartoniert (TB), Englisch
- Verlag: KIT Scientific Publishing
- ISBN-10: 3731508346
- ISBN-13: 9783731508342
- Erscheinungsdatum: 29.11.2018
Sprache:
Englisch
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