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 145 articles , 1 standard article )

Showing results 1 to 20 of 145.
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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. Cox, Pepijn Bastiaan; Tóth, Roland; Petreczky, Mihály: Towards efficient maximum likelihood estimation of LPV-SS models (2018)
  4. 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)
  5. Imani, Mahdi; Braga-Neto, Ulisses M.: Particle filters for partially-observed Boolean dynamical systems (2018)
  6. Liu, Xin; Yang, Xianqiang; Xiong, Weili: A robust global approach for LPV FIR model identification with time-varying time delays (2018)
  7. Mattsson, Per; Zachariah, Dave; Stoica, Petre: Recursive nonlinear-system identification using latent variables (2018)
  8. Mu, Biqiang; Chen, Tianshi: On input design for regularized LTI system identification: power-constrained input (2018)
  9. Mu, Biqiang; Chen, Tianshi; Ljung, Lennart: On asymptotic properties of hyperparameter estimators for kernel-based regularization methods (2018)
  10. Umenberger, Jack; Wågberg, Johan; Manchester, Ian R.; Schön, Thomas B.: Maximum likelihood identification of stable linear dynamical systems (2018)
  11. Yu, Chengpu; Ljung, Lennart; Verhaegen, Michel: Identification of structured state-space models (2018)
  12. Zambrano, J.; Sanchis, J.; Herrero, J. M.; Martínez, M.: WH-EA: an evolutionary algorithm for Wiener-Hammerstein system identification (2018)
  13. 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)
  14. Calafiore, Giuseppe C.; Novara, Carlo; Taragna, Michele: Leading impulse response identification via the elastic net criterion (2017)
  15. Chen, Fengwei; Agüero, Juan C.; Gilson, Marion; Garnier, Hugues; Liu, Tao: EM-based identification of continuous-time ARMA models from irregularly sampled data (2017)
  16. Eckhard, Diego; Bazanella, Alexandre S.; Rojas, Cristian R.; Hjalmarsson, Håkan: Cost function shaping of the output error criterion (2017)
  17. Essa, M. El-Sayed M.; Aboelela, Magdy A. S.; Hassan, M. A. M.: Application of fractional order controllers on experimental and simulation model of hydraulic servo system (2017)
  18. Mu, Biqiang; Bai, Er-Wei; Zheng, Wei Xing; Zhu, Quanmin: A globally consistent nonlinear least squares estimator for identification of nonlinear rational systems (2017)
  19. Ouakasse, Abdelhamid; Mélard, Guy: A new recursive estimation method for single input single output models (2017)
  20. Schoukens, Maarten; Tiels, Koen: Identification of block-oriented nonlinear systems starting from linear approximations: a survey (2017)

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