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 132 articles , 1 standard article )

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  1. Bottegal, Giulio; Castro-Garcia, Ricardo; Suykens, Johan A. K.: A two-experiment approach to Wiener system identification (2018)
  2. Chen, Tianshi: On kernel design for regularized LTI system identification (2018)
  3. Imani, Mahdi; Braga-Neto, Ulisses M.: Particle filters for partially-observed Boolean dynamical systems (2018)
  4. Liu, Xin; Yang, Xianqiang; Xiong, Weili: A robust global approach for LPV FIR model identification with time-varying time delays (2018)
  5. Mattsson, Per; Zachariah, Dave; Stoica, Petre: Recursive nonlinear-system identification using latent variables (2018)
  6. Mu, Biqiang; Chen, Tianshi; Ljung, Lennart: On asymptotic properties of hyperparameter estimators for kernel-based regularization methods (2018)
  7. Yu, Chengpu; Ljung, Lennart; Verhaegen, Michel: Identification of structured state-space models (2018)
  8. Zambrano, J.; Sanchis, J.; Herrero, J. M.; Martínez, M.: WH-EA: an evolutionary algorithm for Wiener-Hammerstein system identification (2018)
  9. 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)
  10. Calafiore, Giuseppe C.; Novara, Carlo; Taragna, Michele: Leading impulse response identification via the elastic net criterion (2017)
  11. Chen, Fengwei; Agüero, Juan C.; Gilson, Marion; Garnier, Hugues; Liu, Tao: EM-based identification of continuous-time ARMA models from irregularly sampled data (2017)
  12. Eckhard, Diego; Bazanella, Alexandre S.; Rojas, Cristian R.; Hjalmarsson, Håkan: Cost function shaping of the output error criterion (2017)
  13. Mu, Biqiang; Bai, Er-Wei; Zheng, Wei Xing; Zhu, Quanmin: A globally consistent nonlinear least squares estimator for identification of nonlinear rational systems (2017)
  14. Ouakasse, Abdelhamid; Mélard, Guy: A new recursive estimation method for single input single output models (2017)
  15. Schoukens, Maarten; Tiels, Koen: Identification of block-oriented nonlinear systems starting from linear approximations: a survey (2017)
  16. 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)
  17. Svensson, Andreas; Schön, Thomas B.: A flexible state-space model for learning nonlinear dynamical systems (2017)
  18. Valenzuela, Patricio E.; Dahlin, Johan; Rojas, Cristian R.; Schön, Thomas B.: On robust input design for nonlinear dynamical models (2017)
  19. Akçay, Hüseyin; Türkay, Semiha: Spectrum estimation with missing values: a regularized nuclear norm minimization approach (2016)
  20. Giles Hooker and James Ramsay and Luo Xiao: CollocInfer: Collocation Inference in Differential Equation Models (2016)

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