Identification of nonlinear dynamical system with synthetic data: a preliminary investigation

M. Mazzoleni, M. Scandella, S. Formentin, F. Previdi

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. [Paper, Code]

Reference

M. Mazzoleni, M. Scandella, S. Formentin, F. Previdi, "Identification of nonlinear dynamical system with synthetic data: a preliminary investigation", 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, 2018, doi: 10.1016/j.ifacol.2018.09.227 , ISSN: 2405-8963, pp. 622 - 627.

Bibtex

@article{MAZZOLENI2018622,
title = "Identification of nonlinear dynamical system with synthetic data: a preliminary investigation",
journal = "IFAC-PapersOnLine",
volume = "51",
number = "15",
pages = "622 - 627",
year = "2018",
note = "18th IFAC Symposium on System Identification SYSID 2018",
issn = "2405-8963",
doi = "https://doi.org/10.1016/j.ifacol.2018.09.227",
author = "M. Mazzoleni and M. Scandella and S. Formentin and F. Previdi",
keywords = "System Identification, Semi-Supervised Learning, Regularization"
}