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: http://freecode.com/)


References in zbMATH (referenced in 164 articles , 1 standard article )

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  1. Abdalmoaty, Mohamed Rasheed-Hilmy; Hjalmarsson, Håkan: Identification of stochastic nonlinear models using optimal estimating functions (2020)
  2. Imani, Mahdi; Dougherty, Edward R.; Braga-Neto, Ulisses: Boolean Kalman filter and smoother under model uncertainty (2020)
  3. Ljung, Lennart; Chen, Tianshi; Mu, Biqiang: A shift in paradigm for system identification (2020)
  4. Scarciotti, Giordano; Jiang, Zhong-Ping; Astolfi, Alessandro: Data-driven constrained optimal model reduction (2020)
  5. Sobolic, Frantisek M.; Aljanaideh, Khaled F.; Bernstein, Dennis S.: A numerical investigation of direct and indirect closed-loop architectures for estimating nonminimum-phase zeros (2020)
  6. Vahid, Milad R.; Hanzon, Bernard; Ober, Raimund J.: Fisher information matrix for single molecules with stochastic trajectories (2020)
  7. Abdalmoaty, Mohamed Rasheed-Hilmy; Hjalmarsson, Håkan: Linear prediction error methods for stochastic nonlinear models (2019)
  8. Gubarev, V. F.; Romanenko, V. D.; Miliavskyi, Yu. L.: Methods for finding a regularized solution when identifying linear multivariable multiconnected discrete systems (2019)
  9. Liu, Xin; Yang, Xianqiang; Zhu, Pengbo; Xiong, Weili: Robust identification of nonlinear time-delay system in state-space form (2019)
  10. Ljung, L.: On convexification of system identification criteria (2019)
  11. Pascu, Valentin; Garnier, Hugues; Ljung, Lennart; Janot, Alexandre: Benchmark problems for continuous-time model identification: design aspects, results and perspectives (2019)
  12. Risuleo, Riccardo Sven; Bottegal, Giulio; Hjalmarsson, Håkan: Modeling and identification of uncertain-input systems (2019)
  13. Shalaby, Raafat; El-Hossainy, Mohammad; Abo-Zalam, Belal: Fractional order modeling and control for under-actuated inverted pendulum (2019)
  14. Tronarp, Filip; Kersting, Hans; Särkkä, Simo; Hennig, Philipp: Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective (2019)
  15. Valenzuela, Patricio E.; Schön, Thomas B.; Rojas, Cristian R.: On model order priors for Bayesian identification of SISO linear systems (2019)
  16. Vallejo LLamas, Pedro M.; Vega, Pastora: Analytical fuzzy predictive control applied to wastewater treatment biological processes (2019)
  17. Bottegal, Giulio; Castro-Garcia, Ricardo; Suykens, Johan A. K.: A two-experiment approach to Wiener system identification (2018)
  18. Chen, Tianshi: On kernel design for regularized LTI system identification (2018)
  19. Cox, Pepijn Bastiaan; Tóth, Roland; Petreczky, Mihály: Towards efficient maximum likelihood estimation of LPV-SS models (2018)
  20. 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)

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Further publications can be found at: http://sigpromu.org/idtoolbox/publications.html