A filtering based recursive least squares estimation algorithm for pseudo-linear auto-regressive systems

Sheng Ding; Rui Ding; Erfu Yang

doi:10.1016/j.jfranklin.2013.10.018

A filtering based recursive least squares estimation algorithm for pseudo-linear auto-regressive systems

Sheng Ding, Rui Ding, Erfu Yang

Design, Manufacturing And Engineering Management

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

This paper uses the filtering technique, transforms a pseudo-linear auto-regressive system into an identification model and presents a new recursive least squares parameter estimation algorithm pseudo-linear auto-regressive systems. The proposed algorithm has a high computational efficiency because the dimensions of its covariance matrices become small compared with the recursive generalized least squares algorithm.

Original language	English
Pages (from-to)	1801-1809
Number of pages	9
Journal	Journal of the Franklin Institute
Volume	351
Issue number	3
Early online date	22 Nov 2013
DOIs	https://doi.org/10.1016/j.jfranklin.2013.10.018
Publication status	Published - Mar 2014

Keywords

filtering
identification model
least squares parameter estimation
signal processing

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abstract = "This paper uses the filtering technique, transforms a pseudo-linear auto-regressive system into an identification model and presents a new recursive least squares parameter estimation algorithm pseudo-linear auto-regressive systems. The proposed algorithm has a high computational efficiency because the dimensions of its covariance matrices become small compared with the recursive generalized least squares algorithm.",

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KW - least squares parameter estimation

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