A new family of concurrent algorithms for adaptive Volterra and linear filters

Author

Chaturvedi, Ajit Kumar ; Sharma, Govind

Author_Institution

Dept. of Electr. Eng., Indian Inst. of Technol., Kanpur, India

Volume

47

Issue

9

fYear

1999

fDate

9/1/1999 12:00:00 AM

Firstpage

2547

Lastpage

2551

Abstract

A novel idea for introducing concurrency in least squares (LS) adaptive algorithms by sacrificing optimality has been proposed. The resultant class of algorithms provides schemes to fill the wide gap in the convergence rates of LS and stochastic gradient (SG) algorithms. It will be particularly useful in the real time implementations of large-order linear and Volterra filters for which both the LS and SG algorithms are unsuited

Keywords

adaptive filters; adaptive signal processing; convergence of numerical methods; filtering theory; gradient methods; least squares approximations; stochastic systems; LS adaptive algorithms; adaptive Volterra filters; adaptive linear filters; concurrent algorithms; convergence rates; large-order filters; least squares adaptive algorithms; parallel recursive least squares algorithm; real time implementations; stochastic gradient algorithms; Adaptive algorithm; Adaptive filters; Algorithm design and analysis; Concurrent computing; Convergence; Equations; Least mean squares methods; Least squares methods; Nonlinear filters; Signal processing algorithms;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.782201

Filename

782201