Paper Info
Title
Stability analysis of adaptive filters with regression vector nonlinearities
Abstract
We present a unified framework to analyze the mean and mean-square stability of a large class of adaptive filters. We do this without obtaining a full transient model, allowing us to acquire sufficient conditions on the stability without assuming a given statistical distribution for the input regressors. We also apply the proposed framework to some popular adaptive filtering schemes, showing that in some cases the sufficient conditions derived are very tight and even necessary too.
Year
DOI
Venue
2011
10.1016/j.sigpro.2011.03.018
Signal Processing
Keywords
Field
DocType
sufficient condition,regression vector nonlinearities,mean-square stability,large class,stability analysis,unified framework,full transient model,statistical distribution,adaptive filters,popular adaptive,proposed framework,adaptive filter,input regressors,step-size parameter,mean stability
Stability criterion,Signal processing,Regression,Control theory,Adaptive method,Adaptive filter,Mean square stability,Mathematics
Journal
Volume
Issue
ISSN
91
8
Signal Processing
Citations 
PageRank 
References 
0
0.34
8
Authors
3
Name
Order
Citations
PageRank
Leonardo Rey Vega110717.14
H. Rey227418.90
Jacob Benesty31386136.42