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Signed Least Mean Kurtosis-Based Adaptive Line Enhancer

Authors: Ji-cheng Ling; Long-qing He; Ye-cai Guo;

Signed Least Mean Kurtosis-Based Adaptive Line Enhancer

Abstract

Based on the aim of the characteristic of error kurtosis and signed-error, a novel algorithm of sign least mean kurtosis based adaptive line enhancer (SLMKBALE) is proposed. Simulation results have shown that the computational load of the proposed SLMKBALE algorithm is much lower than that of the LMKBALE (least mean kurtosis based adaptive line enhancer) and as many as that of LMSBALE (least mean square based adaptive line enhancer), and the SLMKBALE algorithm has better ability to hand non-Gaussian and enhancing signal spectrum in comparison with the LMSBALE, SLMSBALE (signed LMSBALE), LMFBALE (least mean fourth based adaptive line enhancer) and LMKBALE algorithm and that the mean square error (MSE) of the proposed algorithm is the lowest in all algorithms when the MSE converges. Therefore, the SLMKBALE algorithm is useful and reliable

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
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