Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Parameter reduction in nonlinear state-space identification of
  hysteresis   Hysteresis is a highly nonlinear phenomenon, showing up in a wide variety of
science and engineering problems. The identification of hysteretic systems from
input-output data is a challenging task. Recent work on black-box polynomial
nonlinear state-space modeling for hysteresis identification has provided
promising results, but struggles with a large number of parameters due to the
use of multivariate polynomials. This drawback is tackled in the current paper
by applying a decoupling approach that results in a more parsimonious
representation involving univariate polynomials. This work is carried out
numerically on input-output data generated by a Bouc-Wen hysteretic model and
follows up on earlier work of the authors. The current article discusses the
polynomial decoupling approach and explores the selection of the number of
univariate polynomials with the polynomial degree, as well as the connections
with neural network modeling. We have found that the presented decoupling
approach is able to reduce the number of parameters of the full nonlinear model
up to about 50\%, while maintaining a comparable output error level.

37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc