Sign―sign LMS convergence with independent stochastic inputs

Soura Dasgupta; C Richard Johnson Jr; A. M BAKSHO

doi:10.1109/18.50391

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Sign―sign LMS convergence with independent stochastic inputs

Journal article

Peer reviewed

Sign―sign LMS convergence with independent stochastic inputs

Soura Dasgupta, C Richard Johnson Jr and A. M BAKSHO

IEEE transactions on information theory, Vol.36(1), pp.197-201

1990

DOI: 10.1109/18.50391

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Abstract

The sign-sign adaptive least-mean-square (LMS) identifier filter is a computationally efficient variant of the LMS identifier filter. It involves the introduction of signum functions in the traditional LMS update term. Consideration is given to global convergence of parameter estimates offered by this algorithm, to a ball with radius proportional to the algorithm step size for white input sequences, specially from Gaussian and uniform distributions

Applied Sciences

Signal and communications theory

Telecommunications and information theory

Exact sciences and technology

Signal, noise

Information, signal and communications theory

Detection, estimation, filtering, equalization, prediction

Details

Title: Subtitle: Sign―sign LMS convergence with independent stochastic inputs
Creators: Soura Dasgupta - Univ. Iowa, dep. electrical computer eng., Iowa City IA 52242, United States
C Richard Johnson Jr - Cornell University
A. M BAKSHO - Univ. Iowa, dep. electrical computer eng., Iowa City IA 52242, United States
Resource Type: Journal article
Publication Details: IEEE transactions on information theory, Vol.36(1), pp.197-201
DOI: 10.1109/18.50391
ISSN: 0018-9448
eISSN: 1557-9654
Publisher: Institute of Electrical and Electronics Engineers
Language: English
Date published: 1990
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984083259302771

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