New variable step-sizes minimizing mean-square deviation for the lms-type algorithms

Shengkui Zhao, Douglas L. Jones, Suiyang Khoo, Zhihong Man

Research output: Contribution to journalArticle


The least-mean-square-type (LMS-type) algorithms are known as simple and effective adaptation algorithms. However, the LMS-type algorithms have a trade-off between the convergence rate and steady-state performance. In this paper, we investigate a new variable step-size approach to achieve fast convergence rate and low steady-state misadjustment. By approximating the optimal step-size that minimizes the mean-square deviation, we derive variable step-sizes for both the time-domain normalized LMS (NLMS) algorithm and the transform-domain LMS (TDLMS) algorithm. The proposed variable step-sizes are simple quotient forms of the filtered versions of the quadratic error and very effective for the NLMS and TDLMS algorithms. The computer simulations are demonstrated in the framework of adaptive system modeling. Superior performance is obtained compared to the existing popular variable step-size approaches of the NLMS and TDLMS algorithms.

Original languageEnglish (US)
Pages (from-to)2251-2265
Number of pages15
JournalCircuits, Systems, and Signal Processing
Issue number7
StatePublished - Jul 2014


  • Convergence
  • Discrete transforms
  • Least mean square algorithms
  • Mean-square error

ASJC Scopus subject areas

  • Signal Processing
  • Applied Mathematics

Fingerprint Dive into the research topics of 'New variable step-sizes minimizing mean-square deviation for the lms-type algorithms'. Together they form a unique fingerprint.

  • Cite this