バイアス補償拡張最小相関法に基づく変数誤差モデルの同定

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2015/06/01

タイトル(英語): Identification of Errors-In-Variables Models Based on Bias-Compensated Extended Least Correlation Methods

著者名: 池之上　正人（有明工業高等専門学校 電気工学科），和田　清（日本文理大学 工学部）

著者名(英語): Masato Ikenoue (Department of Electrical Engineering, Ariake National College of Technology), Kiyoshi Wada (Faculty of Engineering, Nippon Bunri University)

キーワード: 変数誤差モデル，システム同定，最小相関法，バイアス補償原理　　errors-in-variables models，system identification，least correlation method，bias compensation principle

要約(英語): In this paper, the methods of consistent estimation for identification of linear discrete-time system in the presence of input and output noises, which is usually called “errors-in-variables” (EIV) models, are studied. It is well known that the least squares (LS) method gives biased parameter estimates for EIV situations. To solve this bias problem, the instrumental variable (IV) methods and the least correlation (LC) method are often used. The IV and LC based methods can be applied in more general noise conditions, but these methods suffer from poor accuracy of the estimated parameters because the coefficient matrix of these methods may often become ill-conditioned. In order to obtain numerically stable estimates, the methods presented in this paper use the biased extended LC (XLC) estimates. The biased XLC estimates can be defined by using the extended vectors and the pre-filters. According to the bias compensation principle (BCP) technique, the proposed bias-compensated XLC (BCXLC) methods are developed. The way to reduce the computational load is examined. The results of simulated examples indicate that the proposed methods provide numerically stable and good estimates.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.135 No.6 （2015） 特集：データからの知識発見とその応用

本誌掲載ページ: 686-696 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/135/6/135_686/_article/-char/ja/