Further results in multistage adaptive filtering

Suleyman S. Kozat; Andrew C. Singer

doi:10.1109/icassp.2002.5744048

Further results in multistage adaptive filtering

Suleyman S. Kozat
, Andrew C. Singer

Office of the Dean of the College of Engineering and Applied Sciences

Stony Brook University

University of Illinois at Urbana-Champaign

Research output: Contribution to journal › Conference article › peer-review

5 Scopus citations

Abstract

In this paper, we investigate some of the stochastic properties of two recently introduced multistage adaptive filtering algorithms, namely the LMS-Bayesian and the RLS-Bayesian algorithms. We study probability-1 convergence of these algorithm and derive their final mean squared error for stationary Gaussian time series. We will show that under some general independence assumptions, both algorithms are convergent in a probability-1 sense and achieve the performance of the best algorithm used in the mixture.

Original language	English
Pages (from-to)	II/1329-II/1332
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2
DOIs	https://doi.org/10.1109/icassp.2002.5744048
State	Published - 2002
Event	2002 IEEE International Conference on Acoustic, Speech and Signal Processing - Orlando, FL, United States Duration: May 13 2002 → May 17 2002

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10.1109/icassp.2002.5744048

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title = "Further results in multistage adaptive filtering",

abstract = "In this paper, we investigate some of the stochastic properties of two recently introduced multistage adaptive filtering algorithms, namely the LMS-Bayesian and the RLS-Bayesian algorithms. We study probability-1 convergence of these algorithm and derive their final mean squared error for stationary Gaussian time series. We will show that under some general independence assumptions, both algorithms are convergent in a probability-1 sense and achieve the performance of the best algorithm used in the mixture.",

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AU - Singer, Andrew C.

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AB - In this paper, we investigate some of the stochastic properties of two recently introduced multistage adaptive filtering algorithms, namely the LMS-Bayesian and the RLS-Bayesian algorithms. We study probability-1 convergence of these algorithm and derive their final mean squared error for stationary Gaussian time series. We will show that under some general independence assumptions, both algorithms are convergent in a probability-1 sense and achieve the performance of the best algorithm used in the mixture.

UR - https://www.scopus.com/pages/publications/17444454548

U2 - 10.1109/icassp.2002.5744048

DO - 10.1109/icassp.2002.5744048

M3 - Conference article

SN - 0736-7791

VL - 2

SP - II/1329-II/1332

JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

T2 - 2002 IEEE International Conference on Acoustic, Speech and Signal Processing

Y2 - 13 May 2002 through 17 May 2002

ER -

Further results in multistage adaptive filtering

Abstract

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