Daisy: Database for the Identification of Systems. We point out the existence of a disturbing deficiency in the field of system identification, namely the fact that many results, published in papers, are not reproducible. In many cases, datasets and time series, that are used to illustrate identification methods and algorithms in these publications, are not freely available. We propose to remedy this serious deficiency by setting up a publically accessible website, called DAISY , to which authors can submit datasets that are used to illustrate certain claims and algorithms in their papers. Several additional benefits are discussed as well.

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  1. Hammar, Karima; Djamah, Tounsia; Bettayeb, Maamar: Nonlinear system identification using fractional Hammerstein-Wiener models (2019)
  2. Vervliet, Nico; Debals, Otto; De Lathauwer, Lieven: Exploiting efficient representations in large-scale tensor decompositions (2019)
  3. Rahmani, Mohammad-Reza; Farrokhi, Mohammad: Identification of neuro-fractional Hammerstein systems: a hybrid frequency-/time-domain approach (2018)
  4. Zambrano, J.; Sanchis, J.; Herrero, J. M.; Martínez, M.: WH-EA: an evolutionary algorithm for Wiener-Hammerstein system identification (2018)
  5. Sun, Chuangchuang; Dai, Ran: Rank-constrained optimization and its applications (2017)
  6. Jia, Yanfei; Yang, Xiaodong: A fetal electrocardiogram signal extraction algorithm based on fast one-unit independent component analysis with reference (2016)
  7. Li, Yibing; Nie, Wei; Ye, Fang; Li, Ao: A fetal electrocardiogram signal extraction algorithm based on the temporal structure and the non-Gaussianity (2016)
  8. Verhaegen, Michel; Hansson, Anders: N2SID: nuclear norm subspace identification of innovation models (2016)
  9. Panigrahy, D.; Sahu, P. K.: Extraction of fetal electrocardiogram (ECG) by extended state Kalman filtering and adaptive neuro-fuzzy inference system (ANFIS) based on single channel abdominal recording (2015)
  10. Markovsky, Ivan; Usevich, Konstantin: Software for weighted structured low-rank approximation (2014)
  11. Usevich, Konstantin; Markovsky, Ivan: Optimization on a Grassmann manifold with application to system identification (2014)
  12. Castillo, E.; Morales, D. P.; García, A.; Martínez-Martí, F.; Parrilla, L.; Palma, A. J.: Noise suppression in ECG signals through efficient one-step wavelet processing techniques (2013)
  13. Kalhor, Ahmad; Araabi, Babak N.; Lucas, Caro: Online extraction of main linear trends for nonlinear time-varying processes (2013) ioport
  14. Liu, Zhang; Hansson, Anders; Vandenberghe, Lieven: Nuclear norm system identification with missing inputs and outputs (2013)
  15. Shi, Zhenwei; Zhang, Hongjuan; Jiang, Zhiguo: Hybrid linear and nonlinear complexity pursuit for blind source separation (2012)
  16. Li, Xi-Lin; Adalı, Tülay; Anderson, Matthew: Joint blind source separation by generalized joint diagonalization of cumulant matrices (2011)
  17. Zhao, Yongjian; Liu, Boqiang; Wang, Sen: A robust extraction algorithm for biomedical signals from noisy mixtures (2011) ioport
  18. Li, Changli; Liao, Guisheng: A reference-based blind source extraction algorithm (2010) ioport
  19. Liu, Zhang; Vandenberghe, Lieven: Interior-point method for nuclear norm approximation with application to system identification (2010)
  20. Chen, Jie; Saad, Yousef: On the tensor SVD and the optimal low rank orthogonal approximation of tensors (2009)

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