Abstrakti
In this paper we present a framework for subspace identification of multiple-input multiple output linear time-invariant systems from data corrupted by outliers and piece-wise linear trends. The subspace identification problem is formulated as a sparsity constrained rank minimization problem that is relaxed using the nuclear norm and the l1-norm. The proposed identification method has been validated on a simulated example and on a case study using data from a pilot-plant distillation column.
| Alkuperäiskieli | Ei tiedossa |
|---|---|
| Otsikko | 27th European Symposium on Computer Aided Process Engineering |
| Toimittajat | Antonio Espuña, Moisès Graells, Luis Puigjaner |
| Kustantaja | Elsevier |
| Sivut | 307–312 |
| ISBN (elektroninen) | 978-0-444-63970-7 |
| ISBN (painettu) | 978-0-444-63965-3 |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - 2017 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisuussa |
| Tapahtuma | European Symposium on Computer Aided Process Engineering (ESCAPE) - 27th European Society of Computer-Aided Process Engineering (ESCAPE) Kesto: 1 lokak. 2017 → 5 lokak. 2017 |
Konferenssi
| Konferenssi | European Symposium on Computer Aided Process Engineering (ESCAPE) |
|---|---|
| Ajanjakso | 01/10/17 → 05/10/17 |
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