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An H∞ Optimization and Its Fast Algorithm for Time-Variant System Identification
https://iwate-u.repo.nii.ac.jp/records/9669
https://iwate-u.repo.nii.ac.jp/records/966924cfb62a-6d36-4fb9-a706-7a37c92a0255
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
itsp-v52n5p1335-1342.pdf (386.3 kB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2008-11-06 | |||||||
| タイトル | ||||||||
| タイトル | An H∞ Optimization and Its Fast Algorithm for Time-Variant System Identification | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Fast algorithm | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | FKF | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | FTF | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | H∞ filter | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Kalman filter | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | LMS | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | RLS | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | system identification | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||
| 資源タイプ | journal article | |||||||
| 著者 |
Nishiyama, Kiyoshi
× Nishiyama, Kiyoshi
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| 著者(機関) | ||||||||
| 値 | Dept. of Comput. & Inf. Sci., Iwate Univ. | |||||||
| 登録日 | ||||||||
| 日付 | 2008-11-06 | |||||||
| 書誌情報 |
IEEE TRANSACTIONS ON SIGNAL PROCESSING 巻 52, 号 5, p. 1335-1342, 発行日 2004-01-01 |
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| ISSN | ||||||||
| 収録物識別子タイプ | ISSN | |||||||
| 収録物識別子 | 1053-587X | |||||||
| Abstract | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | In some estimation or identification techniques, a forgetting factor ρ has been used to improve the tracking performance for time-varying systems. However, the value of ρ has been typically determined empirically, without any evidence of optimality. In our previous work, this open problem is solved using the framework of H∞ optimization. The resultant H∞ filter enables the forgetting factor ρ to be optimized through a process noise that is determined by the filter Riccati equation. This paper seeks to further explain the previously derived H∞ filter, giving an H∞ interpretation of its tracking capability. Additionally, a fast algorithm of the H∞ filter, called the fast H∞ filter, is presented when the observation matrix has a shifting property. Finally, the effectiveness of the derived fast algorithm is illustrated for time-variant system identification using several computer simulations. Here, the fast H∞ filter is shown to outperform the well known least-mean-square algorithm and the fast Kalman filter in convergence rate. | |||||||
| 出版者 | ||||||||
| 出版者 | IEEE | |||||||
| 権利 | ||||||||
| 権利情報 | © 2004 IEEE | |||||||
| DOI | ||||||||
| 識別子タイプ | DOI | |||||||
| 関連識別子 | 10.1109/TSP.2004.826156 | |||||||
