Identification of Noisy Input-Output System Using Bias-Compensated Least-Squares Method
| Authors: | Ikenoue Masato, Ariake National College of Technoligy, Japan
Kanae Shunshoku, Kyushu University, Japan
Yang Zi-Jiang, Kyushu University, Japan
Wada Kiyoshi, Kyushu University, Japan |
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| Topic: | 1.1 Modelling, Identification & Signal Processing |
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| Session: | Methods for Errors-in-Variables |
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| Keywords: | Estimation, Identification, Least-squares method |
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Abstract
In this paper, a new bias-compensated least-squares (BCLS) based algorithm is proposed for identification of noisy input-output system. It is well known that BCLS method is based on compensation of asymptotic bias on the least-squares (LS) estimates by making use of noise variances estimates. The main feature of the proposed algorithm is to introduce a generalized least-squares type estimator in order to obtain the good estimates of noise variances. The results of a simulated example indicate that the proposed algorithm provides good estimates.
