Iterative data-driven inference of nonlinearity measures via successive graph approximation
Systems and Control
2020-08-13 v2 Systems and Control
Abstract
In this paper, we establish an iterative data-driven approach to derive guaranteed bounds on nonlinearity measures of unknown nonlinear systems. In this context, nonlinearity measures quantify the strength of the nonlinearity of a dynamical system by the distance of its input-output behaviour to a set of linear models. First, we compute a guaranteed upper bound of these measures by given input-output samples based on a data-based non-parametric set-membership representation of the ground-truth system and local inferences of nonlinearity measures. Second, we propose an algorithm to improve this bound iteratively by further samples of the unknown input-output behaviour.
Cite
@article{arxiv.2004.11746,
title = {Iterative data-driven inference of nonlinearity measures via successive graph approximation},
author = {Tim Martin and Frank Allgöwer},
journal= {arXiv preprint arXiv:2004.11746},
year = {2020}
}