Identification of nonlinear dynamical system with synthetic data: a preliminary investigation
Contributo in Atti di convegno
Data di Pubblicazione:
2018
Citazione:
(2018). Identification of nonlinear dynamical system with synthetic data: a preliminary investigation . Retrieved from http://hdl.handle.net/10446/131594
Abstract:
This paper introduces a new rationale for learning nonlinear dynamical systems. The method makes use of an additional identification dataset, obtained without performing a new experiment on the system under study. The data are generated in an automatical manner, starting from a set of experimentally acquired measurements. In order to leverage the additional generated information, fundamental techniques from the machine learning field known as Semi-Supervised Learning (SSL) are employed and adapted. The problem is then cast as a regularized parametric learning problem. The effectiveness of the proposed approach is assessed on various nonlinear benchmark systems via repeated simulations, comparing the obtained results with a standard regularization method for learning parametric models.
Tipologia CRIS:
1.4.01 Contributi in atti di convegno - Conference presentations
Elenco autori:
Mazzoleni, Mirko; Scandella, Matteo; Formentin, Simone; Previdi, Fabio
Link alla scheda completa:
Titolo del libro:
Proceedings of the 18th IFAC Symposium on System Identification, SYSID 2018. Proceedings
Pubblicato in: