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 88 articles , 1 standard article )

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  1. Akçay, Hüseyin; Türkay, Semiha: Spectrum estimation with missing values: a regularized nuclear norm minimization approach (2016)
  2. 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)
  3. 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)
  4. Verhaegen, Michel; Hansson, Anders: N2SID: nuclear norm subspace identification of innovation models (2016)
  5. Garnier, Hugues: Direct continuous-time approaches to system identification. Overview and benefits for practical applications (2015)
  6. Ko, Sangho; Weyer, Erik; Campi, Marco Claudio: Non-asymptotic model quality assessment of transfer functions at multiple frequency points (2015)
  7. Ma, Xingyun; Ding, Feng: Gradient-based parameter identification algorithms for observer canonical state space systems using state estimates (2015)
  8. Rosich, Albert; Ocampo-Martinez, Carlos: Real-time experimental implementation of predictive control schemes in a small-scale pasteurization plant (2015)
  9. Zhao, Wenxiao; Chen, Han-Fu; Bai, Er-wei; Li, Kang: Kernel-based local order estimation of nonlinear nonparametric systems (2015)
  10. Bruschetta, Mattia; Picci, Giorgio; Saccon, Alessandro: A variational integrators approach to second order modeling and identification of linear mechanical systems (2014)
  11. Li, Junhong; Ding, Feng; Hua, Liang: Maximum likelihood Newton recursive and the Newton iterative estimation algorithms for Hammerstein CARAR systems (2014)
  12. Pillonetto, Gianluigi; Dinuzzo, Francesco; Chen, Tianshi; De Nicolao, Giuseppe; Ljung, Lennart: Kernel methods in system identification, machine learning and function estimation: a survey (2014)
  13. Rotermann, Benedikt; Wilfling, Bernd: Periodically collapsing Evans bubbles and stock-price volatility (2014)
  14. Tiels, Koen; Schoukens, Johan: Wiener system identification with generalized orthonormal basis functions (2014)
  15. Wang, Cheng; Tang, Tao: Recursive least squares estimation algorithm applied to a class of linear-in-parameters output error moving average systems (2014)
  16. Bobal, Vladimir; Kubalcik, Marek; Dostal, Petr; Matejicek, Jakub: Adaptive predictive control of time-delay systems (2013)
  17. Maruta, Ichiro; Sugie, Toshiharu: Projection-based identification algorithm for grey-box continuous-time models (2013)
  18. Baldacchino, Tara; Anderson, Sean R.; Kadirkamanathan, Visakan: Structure detection and parameter estimation for NARX models in a unified EM framework (2012)
  19. Bitzer, Sebastian; Kiebel, Stefan J.: Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks (2012)
  20. Chen, Tianshi; Ohlsson, Henrik; Ljung, Lennart: On the estimation of transfer functions, regularizations and Gaussian processes-revisited (2012)

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