DocumentCode :
938721
Title :
The 
th mean convergence of adaptive filters with stationary dependent random variables
th mean convergence of adaptive filters with stationary dependent random variablesAuthor :
Watanabe, Masafumi
Volume :
30
Issue :
2
fYear :
1984
fDate :
3/1/1984 12:00:00 AM
Firstpage :
134
Lastpage :
140
Abstract :
The th mean convergence of an adaptive filtering algorithm 
is studied, where 
\´s and 
\´s are useful random signals and observation vectors, respectively, and 
is a sequence of positive numbers decreasing to zero. In this work, we suppose that the sequence 
is a stationary mixing sequence ( 
-mixing, 
-mixing, or 
-mixing). Under some additional conditions it is shown that 
converges to 
in the th mean, where is the unique solution of the Wiener-Hopf equation.
th mean convergence of an adaptive filtering algorithm is studied, where \´s and \´s are useful random signals and observation vectors, respectively, and is a sequence of positive numbers decreasing to zero. In this work, we suppose that the sequence is a stationary mixing sequence ( -mixing, -mixing, or -mixing). Under some additional conditions it is shown that converges to in the th mean, where is the unique solution of the Wiener-Hopf equation.Keywords :
Adaptive filters; Adaptive filters; Approximation algorithms; Convergence; Covariance matrix; Equations; Filtering algorithms; Random variables; Statistics; Stochastic processes; Vectors;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/TIT.1984.1056893
Filename :
1056893
Link To Document :
