Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic mode decomposition using a Kalman filter for parameter
  estimation   A novel dynamic mode decomposition (DMD) method based on a Kalman filter is
proposed. This paper explains the fast algorithm of the proposed Kalman filter
DMD (KFDMD) in combination with truncated proper orthogonal decomposition for
many-degree-of-freedom problems. Numerical experiments reveal that KFDMD can
estimate eigenmodes more precisely compared with standard DMD or total
least-squares DMD (tlsDMD) methods for the severe noise condition if the nature
of the observation noise is known, though tlsDMD works better than KFDMD in the
low and medium noise level. Moreover, KFDMD can track the eigenmodes precisely
even when the system matrix varies with time similar to online DMD, and this
extension is naturally conducted owing to the characteristics of the Kalman
filter. In summary, the KFDMD is a promising tool with strong antinoise
characteristics for analyzing sequential datasets.

2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat