Proposed Hybrid Sparse Adaptive Algorithms for System Identification

Abstract For sparse system identification,recent suggested algorithms are -norm Least Mean Square ( -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by addin...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mahmood A. K Abdulsattar, Samer Hussein Ali
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2017
Materias:	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/46aaaa0df3a44edc818af9ab771e9839
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:46aaaa0df3a44edc818af9ab771e9839
record_format	dspace
spelling	oai:doaj.org-article:46aaaa0df3a44edc818af9ab771e98392021-12-02T05:38:10ZProposed Hybrid Sparse Adaptive Algorithms for System Identification10.22153/kej.2017.12.0031818-11712312-0789https://doaj.org/article/46aaaa0df3a44edc818af9ab771e98392017-12-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/350https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 Abstract For sparse system identification,recent suggested algorithms are -norm Least Mean Square ( -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named -ZA-LMS, -RZA-LMS, p-ZA-LMS and p-RZA-LMS that are designed by merging twoconstraints from previous algorithms to improve theconvergence rate and steady state of MSD for sparse system. In this paper, a complete analysis was done for the theoretical operation of proposed algorithms by exited white Gaussian sequence for input signal. The discussion of mean square deviation (MSD) with regard to parameters of algorithms and system sparsity was observed. In addition, in this paper, the correlation between proposed algorithms and the last recent algorithms were presented and the necessary conditions of these proposed algorithms were planned to improve convergence rate. Finally, the results of simulations are compared with theoretical study (?), which is presented to match closely by a wide-range of parameters.. Keywords: Adaptive filter, -LMS, zero-attracting, p-LMS, mean square deviation, Sparse system identification. Mahmood A. K AbdulsattarSamer Hussein AliAl-Khwarizmi College of Engineering – University of BaghdadarticleChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 13, Iss 2 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Mahmood A. K Abdulsattar Samer Hussein Ali Proposed Hybrid Sparse Adaptive Algorithms for System Identification
description	Abstract For sparse system identification,recent suggested algorithms are -norm Least Mean Square ( -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named -ZA-LMS, -RZA-LMS, p-ZA-LMS and p-RZA-LMS that are designed by merging twoconstraints from previous algorithms to improve theconvergence rate and steady state of MSD for sparse system. In this paper, a complete analysis was done for the theoretical operation of proposed algorithms by exited white Gaussian sequence for input signal. The discussion of mean square deviation (MSD) with regard to parameters of algorithms and system sparsity was observed. In addition, in this paper, the correlation between proposed algorithms and the last recent algorithms were presented and the necessary conditions of these proposed algorithms were planned to improve convergence rate. Finally, the results of simulations are compared with theoretical study (?), which is presented to match closely by a wide-range of parameters.. Keywords: Adaptive filter, -LMS, zero-attracting, p-LMS, mean square deviation, Sparse system identification.
format	article
author	Mahmood A. K Abdulsattar Samer Hussein Ali
author_facet	Mahmood A. K Abdulsattar Samer Hussein Ali
author_sort	Mahmood A. K Abdulsattar
title	Proposed Hybrid Sparse Adaptive Algorithms for System Identification
title_short	Proposed Hybrid Sparse Adaptive Algorithms for System Identification
title_full	Proposed Hybrid Sparse Adaptive Algorithms for System Identification
title_fullStr	Proposed Hybrid Sparse Adaptive Algorithms for System Identification
title_full_unstemmed	Proposed Hybrid Sparse Adaptive Algorithms for System Identification
title_sort	proposed hybrid sparse adaptive algorithms for system identification
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2017
url	https://doaj.org/article/46aaaa0df3a44edc818af9ab771e9839
work_keys_str_mv	AT mahmoodakabdulsattar proposedhybridsparseadaptivealgorithmsforsystemidentification AT samerhusseinali proposedhybridsparseadaptivealgorithmsforsystemidentification
_version_	1718400292886675456

Proposed Hybrid Sparse Adaptive Algorithms for System Identification

Ejemplares similares