Résumé
We exploit the key idea that nonlinear system identification is equivalent to linear identification of the so-called Koopman operator. Instead of considering nonlinear system identification in the state space, we obtain a novel linear identification technique by recasting the problem in the infinite-dimensional space of observables. This technique can be described in two main steps. In the first step, similar to a component of the Extended Dynamic Mode Decomposition algorithm, the data are lifted to the infinite-dimensional space and used for linear identification of the Koopman operator. In the second step, the obtained Koopman operator is 'projected back' to the finite-dimensional state space, and identified to the nonlinear vector field through a linear least squares problem. The proposed technique is efficient to recover (polynomial) vector fields of different classes of systems, including unstable, chaotic, and open systems. In addition, it is robust to noise, well-suited to model low sampling rate datasets, and able to infer network topology and dynamics.
langue originale | Anglais |
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titre | 2016 IEEE 55th Conference on Decision and Control, CDC 2016 |
Editeur | Institute of Electrical and Electronics Engineers Inc. |
Pages | 6500-6505 |
Nombre de pages | 6 |
ISBN (Electronique) | 9781509018376 |
Les DOIs | |
Etat de la publication | Publié - 27 déc. 2016 |
Modification externe | Oui |
Evénement | 55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, États-Unis Durée: 12 déc. 2016 → 14 déc. 2016 |
Une conférence
Une conférence | 55th IEEE Conference on Decision and Control, CDC 2016 |
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Pays/Territoire | États-Unis |
La ville | Las Vegas |
période | 12/12/16 → 14/12/16 |