Gaussian process modeling of macroscopic kinetics: a better-tailored kernel for Monod-type kinetics

Autor: Kévin Colin, Håkan Hjalmarsson, Veronique Chotteau

Publikováno v: IFAC-PapersOnLine. 55:397-402

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::659dd0b3b9ab6c3c9ecb7c666e8e7917
https://doi.org/10.1016/j.ifacol.2022.09.127

On the informativity of direct identification experiments in dynamical networks

Autor: Xavier Bombois, Kévin Colin, Paul M.J. Van den Hof, Håkan Hjalmarsson

Publikováno v: Automatica, 148:110742. Elsevier

Data informativity is a crucial property to ensure the consistency of the prediction error estimate. This property has thus been extensively studied in the open-loop and in the closed-loop cases, but has only been briefly touched upon in the dynamic

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e67172d472cbb8dfc1c0771e35a3c9cb
https://research.tue.nl/nl/publications/ef48754c-8e46-47bf-af86-f164bc6d8f70

Data Informativity for the Identification of particular Parallel Hammerstein Systems

Autor: Xavier Bombois, Federico Morelli, Kévin Colin, Laurent Bako

Publikováno v: IFAC-Papers
21th IFAC World Congress
21th IFAC World Congress, Jul 2020, Berlin, Germany. pp.1102-1107, ⟨10.1016/j.ifacol.2020.12.1308⟩

International audience; To obtain a consistent estimate when performing an identication with Prediction Error, it is important that the excitation yields informative data with respect to the chosen model structure. While the characterization of this

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5caeaed933b468dddb6ea8acb04bae2a
https://doi.org/10.1016/j.ifacol.2020.12.1308

Data Informativity for the Closed-Loop Identification of MISO ARX Systems

Autor: Xavier Bombois, Laurent Bako, Kévin Colin

Publikováno v: 19th IFAC Symposium on System Identification
19th IFAC Symposium on System Identification, Jul 2021, Padova, Italy. ⟨10.1016/j.ifacol.2021.08.456⟩

International audience; In the Prediction Error identification framework, it is crucial that the collected data are informative with respect to the chosen model structure to get a consistent estimate. In this work, we focus on the data informativity

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::babff8e1dcb4247890cd518471a39170
https://hal.archives-ouvertes.fr/hal-02971657

Closed-loop Identification of MIMO Systems in the Prediction Error Framework: Data Informativity Analysis

Autor: Kévin Colin, Laurent Bako, Federico Morelli, Xavier Bombois

Publikováno v: Automatica
Automatica, Elsevier, 2020, 121, pp.109171. ⟨10.1016/j.automatica.2020.109171⟩

In the Prediction Error identification framework, it is essential that the experiment yields informative data with respect to the chosen model structure to get a consistent estimate. In this work, we focus on the data informativity property for the i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f366304f13f37d98bc5dfc9931b7e08
https://hal.archives-ouvertes.fr/hal-02351669