Tuning nonlinear state-space models using unconstrained multiple shooting

Autor: Mark Runacres, Joannes Schoukens, Jan Rik Decuyper, Koen Tiels

Publikováno v: IFAC-PapersOnLine. 53:334-340

A persisting challenge in nonlinear dynamical modelling is parameter inference from data. Provided that an appropriate model structure was selected, the identification problem is profoundly affected by a choice of initialisation. A particular challen

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e620163f3c102bf0222208c04c29f376
https://doi.org/10.1016/j.ifacol.2020.12.182

Synthetic wind speed generation for the simulation of realistic diurnal cycles

Autor: Joannes Schoukens, T. De Troyer, Mark Runacres, M. Zivanovic, D. D. Ambrosio

Publikováno v: Academica-e: Repositorio Institucional de la Universidad Pública de Navarra
Universidad Pública de Navarra
Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname

Trabajo presentado a Science of Making Torque from Wind 2020, TORQUE 2020 Synthetic wind-speed generators can provide a detailed characterisation of the wind variability at different time scales. A keen interest in the availability of synthetic wind

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2578cdfb4349d9c25af65d90c81fb07a
https://hdl.handle.net/2454/40055

Polynomial State-Space Model Decoupling for the Identification of Hysteretic Systems

Autor: Jean-Philippe Noël, Koen Tiels, Alireza Fakhrizadeh Esfahani, Joannes Schoukens, Philippe Dreesen

Publikováno v: IFAC-PapersOnLine. 50:458-463

Hysteresis is a nonlinear effect that shows up in a wide variety of engineering and scientific fields. The identification of hysteretic systems from input-output data is an important but challenging question, which has been studied by using both tail

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a69bdd7fca999d8072d0e0a9947ba90
https://doi.org/10.1016/j.ifacol.2017.08.082

Discrete Time Approximation of Continuous Time Nonlinear State Space Models * *This work was supported in part by the Fund for Scientific Research (FWO-Vlaanderen), by the Flemish Government (Methusalem), the Belgian Government through the Inter university Poles of Attraction (IAP VII) Program, and by the ERC advanced grant SNLSID, under contract 320378

Autor: Maarten Schoukens, Rishi Relan, Joannes Schoukens

Publikováno v: IFAC-PapersOnLine. 50:8339-8346

In this paper we discuss the problem of approximating a continuous time nonlinear state space model with an explicit discrete time state space model. Two steps are made to do so. The first one is to replace the classical zero-order-hold (ZOH) excitat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::53ec7fa66b32d2cd5af06196f6325659
https://doi.org/10.1016/j.ifacol.2017.08.1556

Efficient Multidimensional Regularization for Volterra Series Estimation

Autor: Joannes Schoukens, Georgios Birpoutsoukis, Péter Zoltán Csurcsia

This paper presents an efficient nonparametric time domain nonlinear system identification method. It is shown how truncated Volterra series models can be efficiently estimated without the need of long, transient-free measurements. The method is a no

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3e242a7038a4ced172c3fb1583e1201