System Identification Toolbox

SIT (System Identification Toolbox) is a software package, running under either GNU Octave or MATLAB, for estimation of dynamic systems. A wide range of standard estimation approaches are supported. These include the use of non-parametric, subspace-based, and prediction-error algorithms coupled (in the latter case) with either MIMO state space or MISO polynomial model structures. A key feature of the software is the implementation of several new techniques that have been investigated by the authors. These include the estimation of non-linear models, the use of non-standard model parametrizations, and the employment of Expectation Maximization (EM) methods. (Source:

References in zbMATH (referenced in 151 articles , 1 standard article )

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  1. Imani, Mahdi; Dougherty, Edward R.; Braga-Neto, Ulisses: Boolean Kalman filter and smoother under model uncertainty (2020)
  2. Ljung, L.: On convexification of system identification criteria (2019)
  3. Valenzuela, Patricio E.; Schön, Thomas B.; Rojas, Cristian R.: On model order priors for Bayesian identification of SISO linear systems (2019)
  4. Vallejo LLamas, Pedro M.; Vega, Pastora: Analytical fuzzy predictive control applied to wastewater treatment biological processes (2019)
  5. Bottegal, Giulio; Castro-Garcia, Ricardo; Suykens, Johan A. K.: A two-experiment approach to Wiener system identification (2018)
  6. Chen, Tianshi: On kernel design for regularized LTI system identification (2018)
  7. Cox, Pepijn Bastiaan; Tóth, Roland; Petreczky, Mihály: Towards efficient maximum likelihood estimation of LPV-SS models (2018)
  8. Du, Dang-Bo; Zhang, Jian-Xun; Zhou, Zhi-Jie; Si, Xiao-Sheng; Hu, Chang-Hua: Estimating remaining useful life for degrading systems with large fluctuations (2018)
  9. Imani, Mahdi; Braga-Neto, Ulisses M.: Particle filters for partially-observed Boolean dynamical systems (2018)
  10. Li, Junhong; Zheng, Wei Xing; Gu, Juping; Hua, Liang: A recursive identification algorithm for Wiener nonlinear systems with linear state-space subsystem (2018)
  11. Liu, Xin; Yang, Xianqiang; Xiong, Weili: A robust global approach for LPV FIR model identification with time-varying time delays (2018)
  12. Mattsson, Per; Zachariah, Dave; Stoica, Petre: Recursive nonlinear-system identification using latent variables (2018)
  13. Mu, Biqiang; Chen, Tianshi: On input design for regularized LTI system identification: power-constrained input (2018)
  14. Mu, Biqiang; Chen, Tianshi; Ljung, Lennart: On asymptotic properties of hyperparameter estimators for kernel-based regularization methods (2018)
  15. Umenberger, Jack; Wågberg, Johan; Manchester, Ian R.; Schön, Thomas B.: Maximum likelihood identification of stable linear dynamical systems (2018)
  16. Yu, Chengpu; Ljung, Lennart; Verhaegen, Michel: Identification of structured state-space models (2018)
  17. Zambrano, J.; Sanchis, J.; Herrero, J. M.; Martínez, M.: WH-EA: an evolutionary algorithm for Wiener-Hammerstein system identification (2018)
  18. Zhang, Xinguang (ed.); Wu, Yonghong (ed.); Liu, Lishan (ed.); Su, Hua (ed.): Editorial: Recent development on nonlinear methods in function spaces and applications in nonlinear fractional differential equations (2018)
  19. Calafiore, Giuseppe C.; Novara, Carlo; Taragna, Michele: Leading impulse response identification via the elastic net criterion (2017)
  20. Chen, Fengwei; Agüero, Juan C.; Gilson, Marion; Garnier, Hugues; Liu, Tao: EM-based identification of continuous-time ARMA models from irregularly sampled data (2017)

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