### Abstract

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.

Original language | English |
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Title of host publication | IFAC Proceedings Volumes |

Publisher | Elsevier |

Pages | 1390–1395 |

Volume | 41 |

DOIs | |

Publication status | Published - 2008 |

### Bibliographical note

The full text is not available on the repository.### Keywords

- Errors in variables identification
- Recursive identification
- Estimation and filtering

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## Cite this

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