ON WHITENING FOR KRYLOV-PROPORTIONATE NORMALIZED LEAST-MEAN-SQUARE ALGORITHM

被引：2

作者：

Yukawa, Masahiro ^{[1
]}

机构：

[1] RIKEN, Brain Sci Inst, Amari Res Unit, Wako, Saitama, Japan

来源：

2008 IEEE WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING | 2008年

关键词：

adaptive filter; proportionate NLMS; whitening; Krylov subspace;

D O I：

10.1109/MLSP.2008.4685499

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 cast-mean-square (PNLMS), and KPNLMS algorithms is also provided. Numerical examples demonstrate that KPNLMS drastically outperforms TDAF especially in noisy situations.

引用

页码：315 / 320

页数：6

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