A new convergence factor for adaptive filters

Karni, S.; Zeng, G.

doi:10.1109/31.31337

Cited by 64 publications

(24 citation statements)

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“…In the first case the step-size matrix is again a diagonal matrix with the upper bound on each of the diagonal elements, as in Eq. (16). The individual step sizes are calculated as: where ρ is a small positive value allowing to control the convergence process.…”

Section: Vs-lms Algorithm By Mathews Et Almentioning

confidence: 99%

“…24 Next follows the group containing only one parameter to adjust, and this group contains only the NLMS algorithm. 5 Very large is the group of the algorithms needing an upper bound for the step size in addition to one or two parameters; this group includes Shan's algorithms 15 (one of them known as the correlation-LMS), Karni's algorithm, 16 Benveniste's algorithms, 17 and Mathews' algorithms. 19 Three parameters, but without an upper bound for the step size, are also required by Benesty's algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…It is a technique used by majority of the algorithms presented in this paper, namely by Harris, 7 Shan, 15 Karni, 16 Benveniste, 17 Evans, 18 Mathews, 19 Ang, 20 Benesty, 21 Wahab, 22 Hwang 23 and Wang. 24 In many solutions this technique is based on the orthogonality principle, which states that (under some assumptions) the necessary and sufficient condition for the mean-square error to attain its minimum value is that the error signal e(n) and the input signal u(n) are orthogonal.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Vs-lms Algorithm By Mathews Et Almentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Review and Comparison of Variable Step-Size LMS Algorithms

Bismor¹,

Czyż²,

Ogonowski³

2016

IJAV

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“…In these procedures the main point is the filter tap coefficients are not regular, slightly, they change from one iteration to another, in compliance with the noise corruption left over in the output signal. Here are numerous filtering algorithms existing to fill in the coefficients [7] - [9]. Related to all algorithms the Least Mean Square (LMS) technique is the essential technique.…”

Section: Introductionmentioning

confidence: 99%

Sign Regressor based Normalized Adaptive Filters for Speech Enhancement Applications

Jyoshna¹,

Rahman²

2018

IJET

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Removal of noise components of speech signals in mobile applications is an important step to facilitate high resolution signals to the user. Throughout the communication method the speech signals are tainted by numerous non stationary noises. The Least Mean Square (LMS) technique is a fundamental adaptive technique usedbroadly in numerouspurposes as anoutcome of its plainness as well as toughness. In LMS technique, an importantfactor is the step size. It bewell-known that if the union rate of the LMS technique will be rapidif the step size is speedy, but the steady-state mean square error (MSE) will raise. On the rival, for the diminutive step size, the steady state MSE will be minute, but the union rate will be conscious. Thus, the step size offers anexchange among the convergence rate and the steady-state MSE of the LMS technique. Build the step size variable before fixed to recover the act of the LMS technique, explicitly, prefer large step size values at the time of the earlyunion of the LMS technique, and usetiny step size values when the structure is near up to its steady state, which results in Normalized LMS (NLMS) algorithms. In this practice the step size is not stable and changes along with the fault signal at that time. The Less mathematical difficulty of the adaptive filter is extremely attractive in speech enhancement purposes. This drop usually accessible by extract either the input data or evaluation fault. The algorithms depend on an extract of fault or data are Sign Regressor (SR) Algorithms. We merge these sign version to various adaptive noise cancellers. SR Weight NLMS (SRWN-LMS), SR Error NLMS (SRENLMS), SR Unbiased LMS (SRUBLMS) algorithms are individual introduced as a quality factor. These Adaptive noise cancellers are compared with esteem to Signal to Noise Ratio Improvement (SNRI).

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“…The selection of the stepsizes is based on different criteria such as the magnitude of the estimation error (Kwong, 1986), polarity of the successive samples of estimation error (Harris, Chabries and Bishop, 1986) and the cross-correlation of the estimation error with input data (Karni & Zeng, 1989;Shan & Kailath, 1988). Mikhael et al (1986) proposes methods that give the fastest speed of convergence in an attempt to minimize the squared estimation error, but at the expense of large misadjustments in steady state.…”

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confidence: 99%

Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming

2006

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We address the problem of determining optimal stepsizes for estimating parameters in the context of approximate dynamic programming. The sufficient conditions for convergence of the stepsize rules have been known for 50 years, but practical computational work tends to use formulas with parameters that have to be tuned for specific applications. The problem is that in most applications in dynamic programming, observations for estimating a value function typically come from a data series that can be initially highly transient. The degree of transience affects the choice of stepsize parameters that produce the fastest convergence. In addition, the degree of initial transience can vary widely among the value function parameters for the same dynamic program. This paper reviews the literature on deterministic and stochastic stepsize rules, and derives formulas for optimal stepsizes for minimizing estimation error. This formula assumes certain parameters are known, and an approximation is proposed for the case where the parameters are unknown. Experimental work shows that the approximation provides faster convergence than other popular formulas.

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A new convergence factor for adaptive filters

Cited by 64 publications

References 1 publication

Review and Comparison of Variable Step-Size LMS Algorithms

Review and Comparison of Variable Step-Size LMS Algorithms

Sign Regressor based Normalized Adaptive Filters for Speech Enhancement Applications

Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming

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