Unfalsified weighted least squares estimates in set-membership identification

Author

Bai, Er-Wei ; Qiu, Li ; Tempo, Roberto

Author_Institution

Iowa Univ., Iowa City, IA, USA

Volume

45

Issue

1

fYear

1998

fDate

1/1/1998 12:00:00 AM

Firstpage

41

Lastpage

49

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)

Keywords

identification; least squares approximations; set theory; finite impulse response system; noise bound; set membership identification algorithm; weighted least squares estimate; white input signal; Current measurement; Finite impulse response filter; Least squares approximation; Least squares methods; Maximum likelihood estimation; Noise measurement; Recursive estimation; Signal processing; Signal processing algorithms; Time measurement;

fLanguage

English

Journal_Title

Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on

Publisher

ieee

ISSN

1057-7122

Type

jour

DOI

10.1109/81.660752

Filename

660752