On whitening for Krylov-proportionate normalized least-mean-square algorithm

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

The contributions of this paper are twofold. The first is to give theoretical motivation for whitening in the recently proposed adaptive filtering algorithm named the Krylov-proportionate normalized least-mean-square (KPNLMS) algorithm. The second is to present the details of whitening in KPNLMS (In the original work of KPNLMS, the whitening procedure is mentioned but is not described in detail). An interesting connection among the transform-domain adaptive filter (TDAF), proportionate normalized least-mean-square (PNLMS), and KPNLMS algorithms is also provided. Numerical examples demonstrate that KPNLMS drastically outperforms TDAF especially in noisy situations.

本文言語English
ホスト出版物のタイトルProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
ページ315-320
ページ数6
DOI
出版ステータスPublished - 2008 12 1
外部発表はい
イベント2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico
継続期間: 2008 10 162008 10 19

出版物シリーズ

名前Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Other

Other2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
国/地域Mexico
CityCancun
Period08/10/1608/10/19

ASJC Scopus subject areas

  • 人工知能
  • ソフトウェア
  • 電子工学および電気工学

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