書誌事項
- タイトル別名
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- Asymptotically Optimal RLS Adaptive Identification Algorithms
- ゼンキンテキ ニ サイテキ ナ RLS テキオウ ドウテイ アルゴリズム
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A major, and yet unresolved, problem has been the choice of the gain adjusting parameter in some parameter estimation algorithms. This paper presents an adaptive setting method of the forgetting factor for estimating time-varying parameters when the Recursive Least Squares (RLS) algorithm is used. The method is to choose the forgetting factor λ so as to minimize the performance index defined by E {ε2t (λ)} where εt (λ) is the prediction error based on some λ. This is concretely done by solving the minimization problem of a certain object function with respect to λ. It is shown that this object function is unimodal with respect to λ and thus the minimization can be attained by using the stochastic Newton method. As a result, the proposed method becomes asymptotically optimal. The resultant algorithm can be executed by linking the minimization routine with RLS.<BR>Numerical examples indicate acceptable performance.
収録刊行物
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- システム制御情報学会論文誌
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システム制御情報学会論文誌 13 (1), 1-13, 2000
一般社団法人 システム制御情報学会
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詳細情報 詳細情報について
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- CRID
- 1390282680142029824
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- NII論文ID
- 10004472683
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- NII書誌ID
- AN1013280X
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- ISSN
- 2185811X
- 13425668
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- NDL書誌ID
- 4949180
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可
