An algorithm for recursive Frisch scheme system identification of linear single-input single-output errors-in-variables systems is developed. For the update of the estimated model parameters, a recursive bias-compensating least squares algorithm, which is based on the well-known recursive least squares technique, is considered. The estimate of the output measurement noise variance is determined using a conjugate gradient method, which tracks the smallest eigenvalue of a slowly varying matrix. For the update of the input measurement noise estimate, a steepest gradient search is applied. It tracks the minimum of a model selection cost function, which is based on a set of high order Yule-Walker equations.
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- Errors in variables identification
- Recursive identification
- Estimation and filtering
Linden, J. G., Vinsonneau, B., & Burnham, K. J. (2008). Gradient-based approaches for recursive Frisch scheme identification. In IFAC Proceedings Volumes (Vol. 41, pp. 1390–1395). Elsevier. https://doi.org/10.3182/20080706-5-KR-1001.00238