Modified least-squares identification of linear systems with noisy input and output observations

Author

Zheng, Wei Xing

Author_Institution

Dept. of Math., Univ. of Western Sydney, Kingswood, NSW, Australia

Volume

1

fYear

1996

fDate

11-13 Dec 1996

Firstpage

1067

Abstract

In this paper a new type of bias-eliminated least-squares (BELS) based algorithm is proposed for consistent identification of linear systems with noisy input and output measurements. It is shown that estimation of the noise variances can be implemented when the degree of the denominator polynomial of the system transfer function is increased by one. The modified BELS algorithm is attractive and meaningful in that noisy data are used in identification with no prefiltering and a direct estimate of system parameters is given without any parameter transformation

Keywords

least squares approximations; linear systems; noise; parameter estimation; transfer functions; bias-eliminated least-squares based algorithm; denominator polynomial; linear systems; modified least-squares identification; noise variances; noisy input observations; noisy output observations; transfer function; Australia; Linear systems; Mathematics; Measurement standards; Noise measurement; Noise robustness; Parameter estimation; Pollution measurement; Polynomials; Transfer functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1996., Proceedings of the 35th IEEE Conference on

Conference_Location

Kobe

ISSN

0191-2216

Print_ISBN

0-7803-3590-2

Type

conf

DOI

10.1109/CDC.1996.574640

Filename

574640