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Zero-Attracting Function Controlled vsslms algorithm with Analysis

CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2015年第9期34卷 3071-3080页

作者： Turan, Cemil Salman, Mohammad Shukri Mevlana Rumi Univ Elect & Elect Engn Dept Selcuklu Konya Turkey Suleyman Demirel Univ Dept Comp Engn Alma Ata Kazakhstan

The recently proposed function controlled variable step-size least-mean-square (FCvsslms) algorithm has shown high performance in different noise environments. The performance of the algorithm can be improved further if the system is sparse. In this paper, we propose a new algorithm based on the FCvsslms algorithm. The proposed algorithm imposes an approximate penalty in the cost function of the FCvsslms algorithm. We also present the convergence analysis of the proposed algorithm and derive the stability criterion. The performance of the proposed algorithm is compared to those of the variable step-size least-mean-square, more robust variable step-size least-mean-square, FCvsslms algorithm and reweighted zero-attracting least-mean-square algorithm in a system identification setting with an additive white Gaussian noise and additive correlated Gaussian noise. The proposed algorithm has shown high performance compared to the others in terms of the convergence rate and mean square deviation.

关键词： Sparse systems vsslms algorithm MRvsslms algorithm System identification

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A sparse variable step-size LMS algorithm for impulsive noise 3

A sparse variable step-size LMS algorithm for impulsive nois...

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3rd International Conference on Bio-engineering for Smart Technologies (BioSMART)

作者： Salman, Mohammad Shukri El-Sayed, Fahmi Youssef, Ahmed Amer Univ Middle East Coll Engn & Technol Egaila Kuwait

ISBN: (纸本)9781728135786

In sparse system identification, exploiting the sparsity property improves the performance of the least mean square (LMS) algorithm. The improvement is achieved by adding an l(0) or l(1)-norm penalty term to the cost function of the conventional LMS algorithm. However, when the eignenvalue spread of the autocorrelation of the input is relatively large, the performance of the LMS algorithm is poor. For instance, the LMS algorithm cannot perform accurate system identification under the impulsive noise environment because its cost function is not well defined for such scenarios. In this paper, we propose a sparse variable step-size LMS algorithm that employs arctan constraint in the cost function of the algorithm. This constraint imposes a zero attraction of the filter coefficients according to the relative value of each filter coefficient among all the entries. This, in turn, leads to an improved performance when the system is sparse. Different experiments have been conducted to investigate the performance of the proposed algorithm. Simulation results show that the proposed algorithm outperforms the zero attracting LMS (ZA-LMS) and the reweighted ZA-LMS (RZA-LMS) algorithms in a system identification setting under impulsive noise environment.

关键词： LMS algorithm Sparse LMS algorithm vsslms algorithm Sparse system

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A SPARSE FUNCTION CONTROLLED VARIABLE STEP-SIZE LMS algorithm FOR SYSTEM IDENTIFICATION

A SPARSE FUNCTION CONTROLLED VARIABLE STEP-SIZE LMS ALGORITH...

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22nd IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Turan, Cemil Salman, Mohammad Shukri Mevlana Univ Elect & Elect Engn Konya Turkey

ISBN: (纸本)9781479948741

The recently proposed function controlled variable step-size least-mean-square (FCvsslms) algorithm has shown high performance. The performance of the algorithm can be improved further if the system is sparse. In this paper, we propose a new algorithm based on algorithm. The proposed algorithm imposes an approximate l(0) - norm penalty in the cost function of the FCvsslms algorithm. The performance of the proposed algorithm is compared to those of the variable step-size LMS (vsslms) algorithm and FCvsslms algorithm in a system identification setting with an additive white Gaussian noise (AWGN). The proposed algorithm has shown high performance compared to the others in terms of convergence rate and mean-square-deviation (MSD).

关键词： vsslms algorithm Penalty term Sparse systems

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