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 127 articles , 1 standard article )

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  1. Chen, Tianshi: On kernel design for regularized LTI system identification (2018)
  2. Imani, Mahdi; Braga-Neto, Ulisses M.: Particle filters for partially-observed Boolean dynamical systems (2018)
  3. Yu, Chengpu; Ljung, Lennart; Verhaegen, Michel: Identification of structured state-space models (2018)
  4. 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)
  5. Calafiore, Giuseppe C.; Novara, Carlo; Taragna, Michele: Leading impulse response identification via the elastic net criterion (2017)
  6. Chen, Fengwei; Agüero, Juan C.; Gilson, Marion; Garnier, Hugues; Liu, Tao: EM-based identification of continuous-time ARMA models from irregularly sampled data (2017)
  7. Eckhard, Diego; Bazanella, Alexandre S.; Rojas, Cristian R.; Hjalmarsson, Håkan: Cost function shaping of the output error criterion (2017)
  8. Mu, Biqiang; Bai, Er-Wei; Zheng, Wei Xing; Zhu, Quanmin: A globally consistent nonlinear least squares estimator for identification of nonlinear rational systems (2017)
  9. Ouakasse, Abdelhamid; Mélard, Guy: A new recursive estimation method for single input single output models (2017)
  10. Schoukens, Maarten; Tiels, Koen: Identification of block-oriented nonlinear systems starting from linear approximations: a survey (2017)
  11. Singor, S. N.; Boer, A.; Alberts, J. S. C.; Oosterlee, C. W.: On the modelling of nested risk-neutral stochastic processes with applications in insurance (2017)
  12. Svensson, Andreas; Schön, Thomas B.: A flexible state-space model for learning nonlinear dynamical systems (2017)
  13. Valenzuela, Patricio E.; Dahlin, Johan; Rojas, Cristian R.; Schön, Thomas B.: On robust input design for nonlinear dynamical models (2017)
  14. Akçay, Hüseyin; Türkay, Semiha: Spectrum estimation with missing values: a regularized nuclear norm minimization approach (2016)
  15. Giles Hooker and James Ramsay and Luo Xiao: CollocInfer: Collocation Inference in Differential Equation Models (2016)
  16. Guo, Lanjie; Wang, Yanjiao; Wang, Cheng: A recursive least squares algorithm for pseudo-linear ARMA systems using the auxiliary model and the filtering technique (2016)
  17. Kralev, J.; Slavov, Ts.; Petkov, P.: Design and experimental evaluation of robust controllers for a two-wheeled robot (2016)
  18. Larsson, Christian A.; Hägg, Per; Hjalmarsson, Håkan: Generation of signals with specified second-order properties for constrained systems (2016)
  19. Shi, Zhenwei; Wang, Yan; Ji, Zhicheng: Bias compensation based partially coupled recursive least squares identification algorithm with forgetting factors for MIMO systems: application to PMSMs (2016)
  20. Verhaegen, Michel; Hansson, Anders: N2SID: nuclear norm subspace identification of innovation models (2016)

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