A Stochastic Total Least Squares Solution of Adaptive Filtering Problem

An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution...

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Main Authors: Javed, Shazia, Ahmad, Noor Atinah
Format: Article
Language:English
Published: Hindawi Publishing Corporation 2014
Subjects:
Online Access:http://eprints.usm.my/38204/
http://eprints.usm.my/38204/1/A_Stochastic_Total_Least_Squares_Solution_of_Adaptive_Filtering_Problem.pdf
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author Javed, Shazia
Ahmad, Noor Atinah
author_facet Javed, Shazia
Ahmad, Noor Atinah
author_sort Javed, Shazia
building USM Institutional Repository
collection Online Access
description An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence analysis of the algorithm is given to show the global convergence of the proposed algorithm, provided that the stepsize parameter is appropriately chosen. The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. It provides minimum mean square deviation by exhibiting better convergence in misalignment for unknown system identification under noisy inputs.
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spelling usm-382042018-01-03T07:40:42Z http://eprints.usm.my/38204/ A Stochastic Total Least Squares Solution of Adaptive Filtering Problem Javed, Shazia Ahmad, Noor Atinah QA1-939 Mathematics An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence analysis of the algorithm is given to show the global convergence of the proposed algorithm, provided that the stepsize parameter is appropriately chosen. The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. It provides minimum mean square deviation by exhibiting better convergence in misalignment for unknown system identification under noisy inputs. Hindawi Publishing Corporation 2014 Article PeerReviewed application/pdf en http://eprints.usm.my/38204/1/A_Stochastic_Total_Least_Squares_Solution_of_Adaptive_Filtering_Problem.pdf Javed, Shazia and Ahmad, Noor Atinah (2014) A Stochastic Total Least Squares Solution of Adaptive Filtering Problem. Scientific World Journal, 2014 (625280). pp. 1-6. ISSN 2356-6140 http://dx.doi.org/10.1155/2014/625280
spellingShingle QA1-939 Mathematics
Javed, Shazia
Ahmad, Noor Atinah
A Stochastic Total Least Squares Solution of Adaptive Filtering Problem
title A Stochastic Total Least Squares Solution of Adaptive Filtering Problem
title_full A Stochastic Total Least Squares Solution of Adaptive Filtering Problem
title_fullStr A Stochastic Total Least Squares Solution of Adaptive Filtering Problem
title_full_unstemmed A Stochastic Total Least Squares Solution of Adaptive Filtering Problem
title_short A Stochastic Total Least Squares Solution of Adaptive Filtering Problem
title_sort stochastic total least squares solution of adaptive filtering problem
topic QA1-939 Mathematics
url http://eprints.usm.my/38204/
http://eprints.usm.my/38204/
http://eprints.usm.my/38204/1/A_Stochastic_Total_Least_Squares_Solution_of_Adaptive_Filtering_Problem.pdf