Cargando…
Model selection for hybrid dynamical systems via sparse regression
Hybrid systems are traditionally difficult to identify and analyse using classical dynamical systems theory. Moreover, recently developed model identification methodologies largely focus on identifying a single set of governing equations solely from measurement data. In this article, we develop a ne...
Autores principales: | Mangan, N. M., Askham, T., Brunton, S. L., Kutz, J. N., Proctor, J. L. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6451978/ https://www.ncbi.nlm.nih.gov/pubmed/31007544 http://dx.doi.org/10.1098/rspa.2018.0534 |
Ejemplares similares
-
Model selection for dynamical systems via sparse regression and information criteria
por: Mangan, N. M., et al.
Publicado: (2017) -
Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
por: Kaiser, E., et al.
Publicado: (2018) -
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
por: Fasel, U., et al.
Publicado: (2022) -
SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics
por: Kaheman, Kadierdan, et al.
Publicado: (2020) -
Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control
por: Brunton, Steven L., et al.
Publicado: (2016)