Comments, with reply, on "Adaptive algorithms with an automatic gain control feature" by T.J. Shan and T. Kailath

Karni, S.; Zeng, G.

doi:10.1109/31.55079

Cited by 6 publications

(3 citation statements)

References 1 publication

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“…(26), whereas it should be approximately an order smaller for the update according to Eq. (25). The authors also observe that the simpler-to-use update by Eq.…”

Section: Vs-lms Algorithm By Pazaitis Et Almentioning

confidence: 61%

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Review and Comparison of Variable Step-Size LMS Algorithms

Bismor¹,

Czyż²,

Ogonowski³

2016

IJAV

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The inherent feature of the Least Mean Squares (LMS) algorithm is the step size, and it requires careful adjustment. Small step size, required for small excess mean square error, results in slow convergence. Large step size, needed for fast adaptation, may result in loss of stability. Therefore, many modifications of the LMS algorithm, where the step size changes during the adaptation process depending on some particular characteristics, were and are still being developed.The paper reviews seventeen of the best known variable step-size LMS (VS-LMS) algorithms to the degree of detail that allows to implement them. The performance of the algorithms is compared in three typical applications: parametric identification, line enhancement, and adaptive noise cancellation. The paper suggests also one general modification that can simplify the choice of the upper bound for the step size, which is a crucial parameter for many VS-LMS algorithms. u(n)y

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“…(26), whereas it should be approximately an order smaller for the update according to Eq. (25). The authors also observe that the simpler-to-use update by Eq.…”

Section: Vs-lms Algorithm By Pazaitis Et Almentioning

confidence: 61%

“…After comments on their paper 25 the authors added the absolute value and agreed that µ should be bounded (µ(n) < µ max ). 26 The algorithm by Shan et al was simplified afterwards to the form:…”

Section: Vs-lms Algorithm By Shan Et Almentioning

confidence: 99%

Review and Comparison of Variable Step-Size LMS Algorithms

Bismor¹,

Czyż²,

Ogonowski³

2016

IJAV

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“…Based on this technique, many other VSSLMS algorithms with different functions have been developed, such as Versoria function, sine function, and arctan function (Zhang, 2015). Some typical algorithms are proposed by Karni and Zeng (1990), Pazaitis and Constantinides (1999), Benesty et al (2006), Song et al (2015), Haseeb et al (2018), Sun et al (2020), and Li et al (2020). The performance of this type of VSSLMS algorithm relies on the function curve heavily, which makes it not flexible enough to specific systems.…”

Section: Introductionmentioning

confidence: 99%

A novel variable step size least mean square method for adaptive micro-vibration control

Fang

Zhu

Zhang

2021

Journal of Vibration and Control

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The variable step size least mean square algorithm has been suggested since a number of years as a potential solution for improving the performance of least mean square algorithm. In this article, the variable step size least mean square algorithm is classified by the techniques which are used to update step size. Unfortunately, for variable step size least mean square algorithms with forgetting factor, a constant forgetting factor may slow down its convergence speed. For this reason, a variable forgetting factor method for variable step size least mean square is proposed in this article. First, the convergence analysis of a new variable step size least mean square algorithm with the variable forgetting factor is provided. Then, simulations expose the characteristics of this variable forgetting factor method. Last, a micro-vibration control experimental system is established. Four typical variable step size least mean square algorithms and their variable forgetting factor modified version are verified through experiments. The results show that the proposed variable forgetting factor method can effectively improve convergence speed while maintaining the steady-state performance of the variable step size least mean square algorithm with the constant forgetting factor.

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