Paper Info
Title
A reconsideration of improved PNLMS algorithm from metric combining viewpoint
Abstract
In this paper, we show the importance of considering metric in adaptive filtering through a reconsideration of the improved proportionate normalized least mean square (IPNLMS) algorithm for sparse systems from a viewpoint of metric combining. IPNLMS convexly combines a positive-definite diagonal matrix (whose diagonal elements are proportional to the absolute values of the adaptive filter to reflect the system sparsity) with the identity matrix. We present the metric-combining NLMS (MC-NLMS) algorithm and derive, as its special example, the natural PNLMS (NPNLMS) algorithm. NPNLMS can be regarded as a modified version of IPNLMS and we show that NPNLMS is more natural (and performs better) than IPNLMS.
Year
DOI
Venue
2013
10.1109/ACSSC.2013.6810645
Pacific Grove, CA
Keywords
Field
DocType
adaptive filters,least mean squares methods,sparse matrices,IPNLMS algorithm,adaptive filtering,identity matrix,improved PNLMS algorithm,improved proportionate normalized least mean square,metric combining viewpoint,positive definite diagonal matrix,sparse systems
Least mean squares filter,Diagonal,Mathematical optimization,Algorithm design,Computer science,Adaptive system,Algorithm,Adaptive filter,Identity matrix,Diagonal matrix,Sparse matrix
Conference
ISSN
ISBN
Citations 
1058-6393
978-1-4799-2388-5
1
PageRank 
References 
Authors
0.36
5
2
Name
Order
Citations
PageRank
Osamu Toda131.40
Masahiro Yukawa227230.44