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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.
Umfang: XXVIII, 194 S.
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Kenderi, G. 2018. Nonparametric identification of nonlinear dynamic systems. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000085419
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Veröffentlicht am 28. November 2018
Englisch
240
| Paperback | 978-3-7315-0834-2 |