Szczegóły publikacji
Opis bibliograficzny
Fast and robust online dynamic system identification / Andrzej LATOCHA // W: Advanced solutions in diagnostics and fault tolerant control / eds. Jan M. Kościelny, Michał Syfert, Anna Sztyber. — Cham : Springer, cop. 2018. — (Advances in Intelligent Systems and Computing ; ISSN 2194-5357 ; vol. 635). — Zawiera materiały z: DPS'2017 : 13th international conference on Diagnostics of Processes and Systems : Sandomierz, Poland, September 10–13, 2017. — ISBN: 978-3-319-64473-8; e-ISBN: 978-3-319-64474-5. — S. 215–228. — Bibliogr. s. 227–228, Abstr. — Publikacja dostępna online od: 2017-07-29
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 107770 |
|---|---|
| Data dodania do BaDAP | 2017-10-07 |
| Tekst źródłowy | URL |
| DOI | 10.1007/978-3-319-64474-5_18 |
| Rok publikacji | 2018 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 13th international conference on Diagnostics of Processes and Systems |
| Czasopismo/seria | Advances in Intelligent Systems and Computing |
Abstract
A new method is proposed for black-box linear model identification of a dynamic system embedded at a nearly Gaussian noise. The Gaussian process can highlight areas of the output spaces where the prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance of the predicted mean; the input spaces in which we can reconstruct data represent the expected values. This paper proposed a new approach for the online system identification for non-zero initial conditions in the moving window.
