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It is based on least mean square method (LMS).
Otherwise, least mean square regression is calculated for the ratio and NTU data points.
Furthermore, the least mean square (LMS) algorithm is employed to calculate the weight vector.
This algorithm is based on the delayed least mean square (DLMS) method.
Simulation results demonstrate the superiority of the proposed algorithm over existing algorithms such as the filtered-x least mean square, filtered-x logarithmic least mean square, filtered-x normalized least mean square and filtered weight filtered-x normalized least mean square algorithms in terms of convergence rate and noise reduction.
An improved robust variable step-size least mean square (LMS) algorithm is developed in this paper.
The control implementation used a time domain least mean square adaptive algorithm with two error sensors.
Authors claim to propose two algorithms for INBJ systems in above mentioned paper: fractional least mean square (F-LMS) algorithm and auxiliary model fractional least mean square (AM-FLMS) algorithm.
The conventional filtered-x least mean square (FxLMS) algorithm based ANC systems are not designed to handle this situation.
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The coefficients are identified using least-mean-square method in time domain.
Secondly, the criterion of least-mean-square (LMS) is applied to design the desired adaptive notch filter (ANF).
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