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 83 articles , 1 standard article )

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  1. Ko, Sangho; Weyer, Erik; Campi, Marco Claudio: Non-asymptotic model quality assessment of transfer functions at multiple frequency points (2015)
  2. Ma, Xingyun; Ding, Feng: Gradient-based parameter identification algorithms for observer canonical state space systems using state estimates (2015)
  3. Rosich, Albert; Ocampo-Martinez, Carlos: Real-time experimental implementation of predictive control schemes in a small-scale pasteurization plant (2015)
  4. Zhao, Wenxiao; Chen, Han-Fu; Bai, Er-wei; Li, Kang: Kernel-based local order estimation of nonlinear nonparametric systems (2015)
  5. Bruschetta, Mattia; Picci, Giorgio; Saccon, Alessandro: A variational integrators approach to second order modeling and identification of linear mechanical systems (2014)
  6. Li, Junhong; Ding, Feng; Hua, Liang: Maximum likelihood Newton recursive and the Newton iterative estimation algorithms for Hammerstein CARAR systems (2014)
  7. Pillonetto, Gianluigi; Dinuzzo, Francesco; Chen, Tianshi; De Nicolao, Giuseppe; Ljung, Lennart: Kernel methods in system identification, machine learning and function estimation: a survey (2014)
  8. Rotermann, Benedikt; Wilfling, Bernd: Periodically collapsing Evans bubbles and stock-price volatility (2014)
  9. Tiels, Koen; Schoukens, Johan: Wiener system identification with generalized orthonormal basis functions (2014)
  10. Wang, Cheng; Tang, Tao: Recursive least squares estimation algorithm applied to a class of linear-in-parameters output error moving average systems (2014)
  11. Bobal, Vladimir; Kubalcik, Marek; Dostal, Petr; Matejicek, Jakub: Adaptive predictive control of time-delay systems (2013)
  12. Maruta, Ichiro; Sugie, Toshiharu: Projection-based identification algorithm for grey-box continuous-time models (2013)
  13. Baldacchino, Tara; Anderson, Sean R.; Kadirkamanathan, Visakan: Structure detection and parameter estimation for NARX models in a unified EM framework (2012)
  14. Bitzer, Sebastian; Kiebel, Stefan J.: Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks (2012)
  15. Chen, Tianshi; Ohlsson, Henrik; Ljung, Lennart: On the estimation of transfer functions, regularizations and Gaussian processes-revisited (2012)
  16. Chiuso, Alessandro; Pillonetto, Gianluigi: A Bayesian approach to sparse dynamic network identification (2012)
  17. Davis, Ronald E.; Denery, Dallas G.; Kendrick, David A.; Mehra, Raman K.: Introduction to the works of Rodney C. Wingrove: Engineering approaches to macroeconomic modeling (2012)
  18. Gu, Ya; Ding, Ruifeng: Observable state space realizations for multivariable systems (2012)
  19. Huyck, Bart; Ferreau, Hans Joachim; Diehl, Moritz; De Brabanter, Jos; Van Impe, Jan F.M.; De Moor, Bart; Logist, Filip: Towards online model predictive control on a programmable logic controller: practical considerations (2012)
  20. Qiao, Jun-Fei; Han, Hong-Gui: Identification and modeling of nonlinear dynamical systems using a novel self-organizing RBF-based approach (2012)

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