Name
Abstract | ||
|---|---|---|
In this paper, an original approach to frequency identification is explained and demonstrated through an application in the domain of microwave filters. This approach splits into two stages: a stable and causal model of high degree is first computed from the data (completion stage); then, model reduction is performed to get a rational low order model. In the first stage the most is made of the data taking into account the expected behavior of the filter. A reduced order model is then computed by rational H^2 approximation. A new and efficient method has been developed, improved over the years and implemented to solve this problem. It heavily relies on the underlying Hilbert space structure and on a nice parameterization of the optimization set. This approach guarantees the stability of the MIMO approximant of prescribed McMillan degree. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.automatica.2012.10.005 | Automatica |
Keywords | Field | DocType |
Low-pass filters,System identification,Incomplete data,Model reduction,Analytic approximations,Rational approximation,Lossless rational matrices,Parameterization | Hilbert space,Microwave,Mathematical optimization,Parametrization,Control theory,MIMO,Low-pass filter,System identification,Mathematics,Causal model | Journal |
Volume | Issue | ISSN |
49 | 2 | 0005-1098 |
Citations | PageRank | References |
2 | 0.53 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martine Olivi | 1 | 14 | 7.92 |
| Fabien Seyfert | 2 | 2 | 1.54 |
| Jean-Paul Marmorat | 3 | 12 | 1.60 |