Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-driven system analysis of nonlinear systems using polynomial
  approximation   In the context of data-driven system analysis of general nonlinear systems,
there exist only few approaches which often lack of rigorous guarantees, call
for nonconvex optimization, or require knowledge of the basis functions of the
system dynamics. To tackle these drawbacks, we establish in this work a novel
data-based representation of nonlinear functions based on set-memberships for
Taylor polynomials which are derived from finite many noisy samples. By
incorporating the measurement noise and the error of polynomial approximation,
we provide computationally tractable conditions by sum of squares relaxation to
verify non-incremental and incremental dissipativity properties for dynamical
systems with rigorous guarantees and without an explicitly identified model.

26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph