Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Particle-based Gaussian process optimization for input design in
  nonlinear dynamical models   We propose a novel approach to input design for identification of nonlinear
state space models. The optimal input sequence is obtained by maximizing a
scalar cost function of the Fisher information matrix. Since the Fisher
information matrix is unavailable in closed form, it is estimated using
particle methods. In addition, we make use of Gaussian process optimization to
find the optimal input and to mitigate the problem of a large computational
cost incurred by the particle filter, as the method reduces the number of
functional evaluations. Numerical examples are provided to illustrate the
performance of the resulting algorithm.

31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
1
The categories of this paper are: cs