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Beschreibung
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.
Details
| Verlag | KIT Scientific Publishing |
| Ersterscheinung | 28. November 2018 |
| Maße | 24 cm x 17 cm |
| Gewicht | 585 Gramm |
| Format | Softcover |
| ISBN-13 | 9783731508342 |
| Seiten | 241 |
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