System Identification Algorithm for Non-Uniformly Sampled Data * *This study was partially supported by National Institutes of Health (NIH) grant P50 DA039838 and National Science Foundation (NSF) Grants CNS-1329422 and ECCS-1201973 and the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program. The work described is the sole responsibility of the authors and does not necessarily represent the official views of NIH or NSF.-Reference-Cited by-同舟云学术

System Identification Algorithm for Non-Uniformly Sampled Data * *This study was partially supported by National Institutes of Health (NIH) grant P50 DA039838 and National Science Foundation (NSF) Grants CNS-1329422 and ECCS-1201973 and the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program. The work described is the sole responsibility of the authors and does not necessarily represent the official views of NIH or NSF.

Published:2017-07 Issue:1 Volume:50 Page:7296-7301
ISSN:2405-8963
Container-title:IFAC-PapersOnLine
language:en
Short-container-title:IFAC-PapersOnLine

Author:

Bekiroglu Korkut,Lagoa Constantino,Lanza Stephanie T.,Sznaier Mario

Publisher

Elsevier BV

Subject

Control and Systems Engineering

Reference13 articles.

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2. Bekiroglu, K., Yilmaz, B., Lagoa, C., and Sznaier, M. (2014). Parsimonious model identification via atomic norm minimization. In 2014 European Control Conference (ECC), 2392–2397. IEEE. doi: 10.1109/ECC.2014.6862636.

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4. Data-based continuous-time modelling of dynamic systems;Garnier;2011 International Symposium on Advanced Control of Industrial Processes (ADCONIP), (Ml),2011

5. Johansson, R. (2009). Continuous-Time Model Identification and State Estimation Using Non-Uniformly Sampled Data. In 15th IFAC Symposium on System Identification.

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