An LMS adaptive algorithm with a new step-size control equation

Mayyas, K.; Momani, Fadi

doi:10.1016/j.jfranklin.2011.01.003

Cited by 48 publications

(12 citation statements)

References 15 publications

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“…2 shows block diagram of implementation of robust variable step size least mean square (RVSSLMS) algorithm in LabVIEW. The modified variable step size least mean square (MVSSLMS) algorithm has following set of two equations to determine the step size, as proposed in [9]. …”

Section: Implementation Of Variable Step Size Algorithms In Labviewmentioning

confidence: 99%

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Implementation of modified variable step size least mean square algorithm in LabVIEW

Kaur

Singh

Dhaliwal

et al. 2012

2012 IEEE International Conference on Computational Intelligence and Computing Research

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This paper presents implementation of modified variable step size least mean square adaptive algorithm using adaptive filter toolkit of LabVIEW software. User interface is designed using LabVIEW to obtain learning curve for adaptive algorithm. Simulation results are presented to compare the performance of the modified algorithm with the standard least mean square algorithms, Kwong's variable step size algorithm and robust variable step size algorithm. The results show that modified algorithm has good speed of convergence over the existing algorithms.Keywords -LabVIEW, modified variable step size least mean square algorithm (MVSSLMS), Mean square error,Step size

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Section: Implementation Of Variable Step Size Algorithms In Labviewmentioning

confidence: 99%

“…Recently a modified variable step size least mean square (MVSSLMS) algorithm is proposed by Mayyas [9] in which optimization of step-size is done to reduce the mean square error at each time instant. This algorithm is based on approach used in Transform domain least mean square algorithm [10].…”

Section: Introductionmentioning

confidence: 99%

Implementation of modified variable step size least mean square algorithm in LabVIEW

Kaur

Singh

Dhaliwal

et al. 2012

2012 IEEE International Conference on Computational Intelligence and Computing Research

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“…However, the traditional LMS algorithm cannot achieve fast convergence, high tracking accuracy and small steady-state error at the same time, owing to the fixed step size. To solve this problem, many improved LMS algorithms with variable step size have been developed for adaptive filtering [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Design and Application of an Improved Least Mean Square Algorithm for Adaptive Filtering

Zhao-jun¹,

Ru-Gang²

2019

EJEE

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This paper enumerates the strengths and defects of the traditional least mean square (LMS) algorithm for adaptive filtering, and then designs a novel LMS algorithm with variable step size and verifies its performance through simulation. In our algorithm, the step size is no longer adjusted by the square of the error (e2(n)), but by the correlation between the current error and the error of a previous moment e(n-D). In this way, the algorithm becomes less sensitive to the noise with weak autocorrelation, and manages to achieve fast convergence, high time-varying tracking accuracy, and small steady-state error. The simulation results show that our algorithm outperformed the traditional LMS algorithm with fixed step size in convergence speed, tracking accuracy and noise suppression. The research findings provide a new tool for many other fields of adaptive filtering, such as adaptive system identification and adaptive signal separation.

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“…Paper [11] proposed a algorithm with a gradient-based weighted average, but the weight of the system may fluctuate when the amplitude of input signal was too high. Paper [12] used the cost function to get the optimal step-size.…”

Section: Introductionmentioning

confidence: 99%

“…A good algorithm must consider the characters of input signal, but paper [1][2][3][4][5][6][7] were all based on the hypothesis that input signals were all independent to each other. Although paper [8][9][10][11][12] had better performance with correlated input signal, they didn't deal with the input signal and their performance would decline quickly when the correlation coefficient greater than 0.7.In practice, signals are correlated to each other, the performance and the convergence speed of a system would decline sharply if there are no necessary measures. Therefore, in this paper, modified decorrelation principle was used to decorrelate the correlation between input vectors before we identify the system.…”

Section: Introductionmentioning

confidence: 99%

A variable step-size adaptive system identification algorithm based on modified decorrelation principle

Ge¹,

Wan²,

Zhao³

2016

Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials

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To overcome the defects of traditional adaptive Least-mean-square(LMS) algorithm, figure out the problem that the existing algorithms' system identification abilities would decline while their input signals are correlated with each other, a novel variable step-size and variable parameters adaptive system identification algorithm based on modified decorrelation principle is proposed. The design theories and process, the selection principles of the adjusting parameters and the performance analysis of the algorithm are all given below. In the algorithm, the rate of its convergence is improved by the modified decorrelation principle. Variable step size in the algorithm is based on hyperbolic tangent function, it is adjusted by the mean value of the gradient vector, so the system can modify it step size by the energy of the input signals and the real-time error. The variable step size also introduce the self-correlation of the real-time error to retrain the influence of the independent noises. Theory and simulation results verified the system identification abilities and the superiorities of the algorithm under different circumstances.

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An LMS adaptive algorithm with a new step-size control equation

Cited by 48 publications

References 15 publications

Implementation of modified variable step size least mean square algorithm in LabVIEW

Implementation of modified variable step size least mean square algorithm in LabVIEW

Design and Application of an Improved Least Mean Square Algorithm for Adaptive Filtering

A variable step-size adaptive system identification algorithm based on modified decorrelation principle

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