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 o...
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| Format: | Online |
| Language: | English |
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Hindawi Publishing Corporation
2014
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3932194/ |
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pubmed-39321942014-03-31 A Stochastic Total Least Squares Solution of Adaptive Filtering Problem Javed, Shazia Ahmad, Noor Atinah Research Article 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 2014-02-03 /pmc/articles/PMC3932194/ /pubmed/24688412 http://dx.doi.org/10.1155/2014/625280 Text en Copyright © 2014 S. Javed and N. A. Ahmad. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Javed, Shazia Ahmad, Noor Atinah |
| spellingShingle |
Javed, Shazia Ahmad, Noor Atinah A Stochastic Total Least Squares Solution of Adaptive Filtering Problem |
| author_facet |
Javed, Shazia Ahmad, Noor Atinah |
| author_sort |
Javed, Shazia |
| title |
A Stochastic Total Least Squares Solution of Adaptive Filtering Problem |
| title_short |
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_sort |
stochastic total least squares solution of adaptive filtering problem |
| 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. |
| publisher |
Hindawi Publishing Corporation |
| publishDate |
2014 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3932194/ |
| _version_ |
1612060993551073280 |