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 178 articles , 1 standard article )

Showing results 1 to 20 of 178.
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  1. Hallemans, Noël; Pintelon, Rik; Joukovsky, Boris; Peumans, Dries; Lataire, John: FRF estimation using multiple kernel-based regularisation (2022)
  2. Shakib, Mohammad Fahim; Pogromsky, Alexander Yu.; Pavlov, Alexey; van de Wouw, Nathan: Computationally efficient identification of continuous-time Lur’e-type systems with stability guarantees (2022)
  3. Vásquez, Sandra; Kinnaert, Michel; Pintelon, Rik: On the consistency and asymptotic normality of discrete-time LTI models identified from concatenated data sets (2022)
  4. Chen, Xin; Zhao, Shunyi; Liu, Fei: Robust identification of linear ARX models with recursive EM algorithm based on Student’s t-distribution (2021)
  5. Forgione, Marco; Piga, Dario: Continuous-time system identification with neural networks: model structures and fitting criteria (2021)
  6. Guo, Lanjie; Wang, Hao; Lin, Zhe: Recursive least-squares algorithm for a characteristic model with coloured noise by means of the data filtering technique (2021)
  7. Liu, Qie; Tang, Xinming; Li, Junhao; Zeng, Jianxue; Zhang, Ke; Chai, Yi: Identification of Wiener-Hammerstein models based on variational Bayesian approach in the presence of process noise (2021)
  8. Luan Vinicius Fiorio, Chrystian Lenon Remes, Yales Romulo de Novaes: mpulseest: A Python package for non-parametric impulse response estimation with input-output data (2021) not zbMATH
  9. Masti, Daniele; Bemporad, Alberto: Learning nonlinear state-space models using autoencoders (2021)
  10. Wills, Adrian G.; Schön, Thomas B.: Stochastic quasi-Newton with line-search regularisation (2021)
  11. Zheng, Man; Ohta, Yoshito: Bayesian positive system identification: truncated Gaussian prior and hyperparameter estimation (2021)
  12. Abdalmoaty, Mohamed Rasheed-Hilmy; Hjalmarsson, Håkan: Identification of stochastic nonlinear models using optimal estimating functions (2020)
  13. Imani, Mahdi; Dougherty, Edward R.; Braga-Neto, Ulisses: Boolean Kalman filter and smoother under model uncertainty (2020)
  14. Jin, Yongze; Xie, Guo; Chen, Pang; Hei, Xinhong; Ji, Wenjiang; Zhao, Jinwei: High-speed train emergency brake modeling and online identification of time-varying parameters (2020)
  15. Ljung, Lennart; Chen, Tianshi; Mu, Biqiang: A shift in paradigm for system identification (2020)
  16. Pyrkin, A. A.; Vedyakov, A. A.; Ortega, R.; Bobtsov, A. A.: A robust adaptive flux observer for a class of electromechanical systems (2020)
  17. Scarciotti, Giordano; Jiang, Zhong-Ping; Astolfi, Alessandro: Data-driven constrained optimal model reduction (2020)
  18. Sobolic, Frantisek M.; Aljanaideh, Khaled F.; Bernstein, Dennis S.: A numerical investigation of direct and indirect closed-loop architectures for estimating nonminimum-phase zeros (2020)
  19. Vahid, Milad R.; Hanzon, Bernard; Ober, Raimund J.: Fisher information matrix for single molecules with stochastic trajectories (2020)
  20. Abdalmoaty, Mohamed Rasheed-Hilmy; Hjalmarsson, Håkan: Linear prediction error methods for stochastic nonlinear models (2019)

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