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Titolo:
SET-MEMBERSHIP FILTERING AND A SET-MEMBERSHIP NORMALIZED LMS ALGORITHM WITH AN ADAPTIVE STEP-SIZE
Autore:
GOLLAMUDI S; NAGARAJ S; KAPOOR S; HUANG YF;
Indirizzi:
UNIV NOTRE DAME,DEPT ELECT ENGN,LAB IMAGE & SIGNAL ANAL NOTRE DAME IN46556
Titolo Testata:
IEEE signal processing letters
fascicolo: 5, volume: 5, anno: 1998,
pagine: 111 - 114
SICI:
1070-9908(1998)5:5<111:SFAASN>2.0.ZU;2-7
Fonte:
ISI
Lingua:
ENG
Soggetto:
BOUNDED NOISE; IDENTIFICATION;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
11
Recensione:
Indirizzi per estratti:
Citazione:
S. Gollamudi et al., "SET-MEMBERSHIP FILTERING AND A SET-MEMBERSHIP NORMALIZED LMS ALGORITHM WITH AN ADAPTIVE STEP-SIZE", IEEE signal processing letters, 5(5), 1998, pp. 111-114

Abstract

Set-membership identification (SMI) theory is extended to the more general problem of linear-in-parameters filtering by defining a set-membership specification, as opposed to a bounded noise assumption, This sets the framework for several important filtering problems that are not modeled by a ''true'' unknown system with bounded noise, such as adaptive equalization, to exploit the unique advantages of SMI algorithms. A recursive solution for set membership filtering is derived that resembles a variable step size normalized least mean squares (NLMS) algorithm, Interesting properties of the algorithm, such as asymptotic cessation of updates and monotonically nonincreasing parameter error, areestablished. Simulations show significant performance improvement in varied environments with a greatly reduced number of updates.

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Documento generato il 07/07/20 alle ore 21:59:12