DocumentCode :
1251540
Title :
Learning curves for LMS and regular Gaussian processes
Author :
Hriljac, Paul
Author_Institution :
Coll. of Eng., Embry-Riddle Univ., Prescott, AZ, USA
Volume :
47
Issue :
2
fYear :
2002
fDate :
2/1/2002 12:00:00 AM
Firstpage :
284
Lastpage :
289
Abstract :
Uses methods due to Guo, Ljung, and Wang (1997) to obtain explicit bounds on the error of the LMS algorithm used in a linear prediction of a signal using previous values of that signal. The signal is assumed to be a mean-zero Gaussian regular stationary random process. The bounds are then used to construct learning curves for the LMS algorithm in situations where the statistics of the process are only partially known
Keywords :
Gaussian processes; least mean squares methods; matrix algebra; prediction theory; random processes; LMS; error bounds; learning curves; least mean squares process; linear prediction; mean-zero Gaussian regular stationary random process; Convergence; Covariance matrix; Gaussian processes; Least squares approximation; Linear matrix inequalities; Prediction algorithms; Random processes; Signal processing; Statistics; Stochastic processes;
fLanguage :
English
Journal_Title :
Automatic Control, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9286
Type :
jour
DOI :
10.1109/9.983358
Filename :
983358
Link To Document :
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