A variable step (VS) adaptive filter algorithm

Harris, Richard; Chabries, Douglas M.; Bishop, F. Avery

doi:10.1109/tassp.1986.1164814

Cited by 349 publications

(154 citation statements)

References 10 publications

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“…On the other hand, an LMS algorithm with a small step size tends to converge slowly but the MSE becomes small. To provide solutions to this tradeoff, there have been many research performed, including the algorithms using Variable Step Size LMS (VSSLMS) [3,4,5,6] and those with Multiple Combined LMS (MCLMS) filters [7,8]. In this letter we propose a Categorized Variable Step Size Least Mean Square (CVSSLMS) algorithm as a novel VSSLMS algorithm and a Combined CVSSLMS (CCVSSLMS) as an extension by combining with a fixed step size LMS filter.…”

Section: Introductionmentioning

confidence: 99%

Novel LMS algorithms based on status categorization

Kim

Chon

Lim

et al. 2009

IEICE Electron. Express

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Least Mean Square (LMS) is an effective adaptive filtering algorithm with advantages of robustness and simplicity. In this paper, we propose two new algorithms, Categorized Variable Step Size LMS (CVSSLMS) and Combined CVSSLMS (CCVSSLMS), based on the categorization of filter status. The step sizes of the proposed algorithms are dynamically updated by optimization for each state. Experiment results show that the proposed algorithms outperform conventional LMS algorithms in both simplicity and robustness.

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Section: Introductionmentioning

confidence: 99%

Novel LMS algorithms based on status categorization

Kim

Chon

Lim

et al. 2009

IEICE Electron. Express

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“…Several designs in this group have the disadvantage of being batch algorithms, which are not appropriate when on-line learning is required. There are also several schemes that manage the step size in a stochastic manner (see, for instance, [8][9][10][11][12][13]). All these procedures obtain good results and are computationally efficient, but they add some hyperparameters which must be fixed to a priori values.…”

Section: Introductionmentioning

confidence: 99%

Plant identification via adaptive combination of transversal filters

et al. 2006

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For least mean square (LMS) algorithm applications, it is important to improve the speed of convergence vs the residual error trade off imposed by the selection of a certain value for the step size. In this paper, we propose to use a mixture approach, adaptively combining two independent LMS filters with large and small step sizes to obtain fast convergence with low misadjustment during stationary periods. Some plant identification simulation examples show the effectiveness of our method when compared to previous variable step size approaches. This combination approach can be straightforwardly extended to other kinds of filters, as it is illustrated with a convex combination of recursive least squares (RLS) filters..

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“…Many VSSLMS algorithms have been proposed to improve the performance of the LMS algorithm. An important class of VSSLMS algorithms is the one in which the step size is updated using the gradient vector [3] [4][5] [6] [7]. In our opinion, all of these algorithms utilize two properties of the gradient vector:…”

Section: Introductionmentioning

confidence: 99%