Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Initialization of Nonlinear LFR Model Identification with the
  Best Linear Approximation   Balancing the model complexity and the representation capability towards the
process to be captured remains one of the main challenges in nonlinear system
identification. One possibility to reduce model complexity is to impose
structure on the model representation. To this end, this work considers the
linear fractional representation framework. In a linear fractional
representation the linear dynamics and the system nonlinearities are modeled by
two separate blocks that are interconnected with one another. This results in a
structured, yet flexible model structure. Estimating such a model directly from
input-output data is not a trivial task as the involved optimization is
nonlinear in nature. This paper proposes an initialization scheme for the model
parameters based on the best linear approximation of the system and shows that
this approach results in high quality models on a set of benchmark data sets.

13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys