Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Iterative data-driven inference of nonlinearity measures via successive
  graph approximation   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.

32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn