NDLI: Proportionate-Type Normalized Least Mean Square Algorithms With Gain Allocation Motivated by Mean-Square-Error Minimization for White Input

Content Provider	IEEE Xplore Digital Library
Author	Wagner, K. Doroslovački, M.
Copyright Year	1991
Abstract	In the past, ad hoc methods have been used to choose gains in proportionate-type normalized least mean-square algorithms without strong theoretical under-pinnings. In this correspondence, a theoretical framework and motivation for adaptively choosing gains is presented, such that the mean-square error will be minimized at any given time. As a result of this approach, a new optimal proportionate-type normalized least mean-square algorithm is introduced. A computationally simplified version of the theoretical optimal algorithm is derived as well. Both of these algorithms require knowledge of the mean-square weight deviations. Feasible implementations, which estimate the mean-square weight deviations, are presented. The performance of these new feasible algorithms are compared to the performance of standard adaptive algorithms when operating with sparse, non-sparse, and time-varying impulse responses, when the input signal is white. Specifically, we consider the transient and steady-state mean-square errors as well as the overall computational complexity of each algorithm.
Sponsorship	IEEE Signal Processing Society
Starting Page	2410
Ending Page	2415
Page Count	6
File Size	570729
File Format	PDF
ISSN	1053587X
Volume Number	59
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Signal processing algorithms Noise Steady-state Computational complexity Approximation algorithms Convergence Approximation methods least mean-square methods Adaptive filters adaptive signal processing echo suppression identification
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Proportionate adaptive algorithms for network echo cancellation

Krylov-Proportionate Adaptive Filtering Techniques Not Limited to Sparse Systems

Improving convergence of the PNLMS algorithm for sparse impulse response identification

Unbiased Model Combinations for Adaptive Filtering

A general class of nonlinear normalized adaptive filtering algorithms

A Nonparametric VSS NLMS Algorithm

Interference-Normalized Least Mean Square Algorithm

On the Steady-State Analysis of PNLMS-Type Algorithms for Correlated Gaussian Input Data

Probability Density of Weight Deviations Given Preceding Weight Deviations for Proportionate-Type LMS Adaptive Algorithms

Proportionate-Type Normalized Least Mean Square Algorithms With Gain Allocation Motivated by Mean-Square-Error Minimization for White Input

Similar Documents

Proportionate adaptive algorithms for network echo cancellation

Krylov-Proportionate Adaptive Filtering Techniques Not Limited to Sparse Systems

Improving convergence of the PNLMS algorithm for sparse impulse response identification

Unbiased Model Combinations for Adaptive Filtering

A general class of nonlinear normalized adaptive filtering algorithms

A Nonparametric VSS NLMS Algorithm

Interference-Normalized Least Mean Square Algorithm

On the Steady-State Analysis of PNLMS-Type Algorithms for Correlated Gaussian Input Data

Probability Density of Weight Deviations Given Preceding Weight Deviations for Proportionate-Type LMS Adaptive Algorithms

Proportionate-Type Normalized Least Mean Square Algorithms With Gain Allocation Motivated by Mean-Square-Error Minimization for White Input