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NLMS is More Robust to Input-Correlation Than LMS: A Proof.

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Title: NLMS is More Robust to Input-Correlation Than LMS: A Proof.
Authors: Ali, Anum1 (AUTHOR) anumali@utexas.edu, Moinuddin, Muhammad2 (AUTHOR) mmsansari@kau.edu.sa, Al-Naffouri, Tareq Y.3 (AUTHOR) tareq.alnaffouri@kaust.edu.sa
Source: IEEE Signal Processing Letters. 2022, Vol. 29, p279-283. 5p.
Subjects: Least squares, Adaptive filters
Abstract: In this work, we comparatively analyze the least mean squares (LMS) algorithm and the normalized least mean squares (NLMS) algorithm. We use the input moment matrices for comparison as the mean-square behavior of both algorithms is determined by the input moment matrices. First, we derive the closed-form expressions of the input moment matrices of the NLMS. Second, we do a numerical and theoretical comparison of the input moment matrices of the LMS and the NLMS. The analysis shows why the performance of the NLMS is less sensitive to the changes in eigenvalue-spread (of the input-correlation matrix) than the LMS. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Signal Processing Letters is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 155383886
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PubType: Academic Journal
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  Data: NLMS is More Robust to Input-Correlation Than LMS: A Proof.
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  Data: <searchLink fieldCode="AR" term="%22Ali%2C+Anum%22">Ali, Anum</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> anumali@utexas.edu</i><br /><searchLink fieldCode="AR" term="%22Moinuddin%2C+Muhammad%22">Moinuddin, Muhammad</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> mmsansari@kau.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Al-Naffouri%2C+Tareq+Y%2E%22">Al-Naffouri, Tareq Y.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> tareq.alnaffouri@kaust.edu.sa</i>
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Signal+Processing+Letters%22">IEEE Signal Processing Letters</searchLink>. 2022, Vol. 29, p279-283. 5p.
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  Data: <searchLink fieldCode="DE" term="%22Least+squares%22">Least squares</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+filters%22">Adaptive filters</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this work, we comparatively analyze the least mean squares (LMS) algorithm and the normalized least mean squares (NLMS) algorithm. We use the input moment matrices for comparison as the mean-square behavior of both algorithms is determined by the input moment matrices. First, we derive the closed-form expressions of the input moment matrices of the NLMS. Second, we do a numerical and theoretical comparison of the input moment matrices of the LMS and the NLMS. The analysis shows why the performance of the NLMS is less sensitive to the changes in eigenvalue-spread (of the input-correlation matrix) than the LMS. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Signal Processing Letters is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1109/LSP.2021.3134141
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 5
        StartPage: 279
    Subjects:
      – SubjectFull: Least squares
        Type: general
      – SubjectFull: Adaptive filters
        Type: general
    Titles:
      – TitleFull: NLMS is More Robust to Input-Correlation Than LMS: A Proof.
        Type: main
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      – PersonEntity:
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            NameFull: Ali, Anum
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          Name:
            NameFull: Moinuddin, Muhammad
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            NameFull: Al-Naffouri, Tareq Y.
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            – D: 01
              M: 01
              Text: 2022
              Type: published
              Y: 2022
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              Value: 29
          Titles:
            – TitleFull: IEEE Signal Processing Letters
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