Extending the Frisch scheme for errors‐in‐variables identification to correlated output noise

Extending the Frisch scheme for errors-in-variables identification to correlated output noise

[1]  T. Söderström,et al.  A useful input parameterization for optimal experiment design , 1982 .

[2]  Sabine Van Huffel,et al.  Recent advances in total least squares techniques and errors-in-variables modeling , 1997 .

[3]  Torsten Söderström,et al.  Errors-in-variables methods in system identification , 2018, Autom..

[4]  Torsten Söderström,et al.  Perspectives on errors-in-variables estimation for dynamic systems , 2002, Signal Process..

[5]  Wei Xing Zheng,et al.  Convergence properties of bias‐eliminating algorithms for errors‐in‐variables identification , 2005 .

[6]  Umberto Soverini,et al.  A New Criterion in EIV Identification and Filtering Applications , 2003 .

[7]  T. Soderstrom,et al.  Accuracy analysis of the Frisch estimates for identifying errors-in-variables systems , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[8]  Petre Stoica,et al.  Uniqueness of the maximum likelihood estimates of ARMA model parameters--An elementary proof , 1982 .

[9]  W. Zheng.  Transfer function estimation from noisy input and output data , 1998 .

[10]  Umberto Soverini,et al.  The frisch scheme in dynamic system identification , 1990, Autom..

[11]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[12]  Torsten Söderström,et al.  Identification of stochastic linear systems in presence of input noise , 1981, Autom..

[13]  Wei Xing Zheng,et al.  A Simplified Form of the Bias-Eliminating Least Squares Method for Errors-in-Variables Identification , 2007, IEEE Transactions on Automatic Control.

[14]  R. Allen,et al.  Statistical Confluence Analysis by means of Complete Regression Systems , 1935 .

[15]  Torsten Söderström,et al.  Identification Methods of Dynamic Systems in Presence of Input Noise , 2000 .