A unified framework for EIV identification methods when the measurement noises are mutually correlated

Author:

Söderström Torsten,Diversi Roberto,Soverini Umberto

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Reference32 articles.

1. Beghelli, S., Castaldi, P., Guidorzi, R., & Soverini, U. (1993). A robust criterion for model selection in identification from noisy data. In Proc. 9th International conference on systems engineering (pp. 480–484). Las Vegas, Nevada, USA.

2. Beghelli, S., Castaldi, P., & Soverini, U. (1997). A frequential approach for errors-in-variables models. In Proc. European control conference, ECC’97. Brussels, Belgium, July 1–4.

3. Bias-eliminating least-squares identification of errors-in-variables models with mutually correlated noises;Diversi;International Journal of Adaptive Control and Signal Processing,2013

4. Diversi, R., Guidorzi, R., & Soverini, U. (2003). A new criterion in EIV identification and filtering applications. In Proc. 13th IFAC symposium on system identification (pp. 1993–1998). Rotterdam, The Netherlands, August 27–29.

5. Diversi, R., Guidorzi, R., & Soverini, U. (2004). Frisch scheme-based algorithms for EIV identification. In Proc. 12th IEEE Mediterranean conference on control and automation. Kusadasi, Turkey, June.

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