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