Unfalsified weighted least squares estimates in set-membership identification

Er Wei Bai

doi:10.1109/81.660752

Unfalsified weighted least squares estimates in set-membership identification

Er Wei Bai^*

^*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

It is well known that the weighted least squares (WLS) identification algorithm provides estimates that are in general not in the membership set and in this sense are falsified estimates. This paper shows that: 1) if the noise bound is known, the WLS estimates can be made to lie in or converge to the membership set by choosing the weights properly and 2) if the noise bound is unknown, the same results can still be achieved by using white input signals for finite impulse response systems (FIR).

Original language	English
Pages (from-to)	41-49
Number of pages	9
Journal	IEEE Transactions on Circuits and Systems I: Regular Papers
Volume	45
Issue number	1
DOIs	https://doi.org/10.1109/81.660752
Publication status	Published - 1998
Externally published	Yes

Keywords

Identification
Least square
Set membership

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10.1109/81.660752

Cite this

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abstract = "It is well known that the weighted least squares (WLS) identification algorithm provides estimates that are in general not in the membership set and in this sense are falsified estimates. This paper shows that: 1) if the noise bound is known, the WLS estimates can be made to lie in or converge to the membership set by choosing the weights properly and 2) if the noise bound is unknown, the same results can still be achieved by using white input signals for finite impulse response systems (FIR).",

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AB - It is well known that the weighted least squares (WLS) identification algorithm provides estimates that are in general not in the membership set and in this sense are falsified estimates. This paper shows that: 1) if the noise bound is known, the WLS estimates can be made to lie in or converge to the membership set by choosing the weights properly and 2) if the noise bound is unknown, the same results can still be achieved by using white input signals for finite impulse response systems (FIR).

KW - Identification

KW - Least square

KW - Set membership

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Unfalsified weighted least squares estimates in set-membership identification

Abstract

Keywords

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