Optimized LMS algorithm for system identification and noise cancellation

Optimization by definition is the action of making most effective or the best use of a resource or situation and that is required almost in every field of engineering. In this work, the optimization of Least Mean square (LMS) algorithm is carried out with the help of Particle Swarm Optimization (PSO...

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Autores principales:	Ling Qianhua, Ikbal Mohammad Asif, Kumar P.
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	ant colony optimization particle swarm optimization least mean square step size noise cancellation system identification Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/7295af61d46349b892ec1834496975c8
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id	oai:doaj.org-article:7295af61d46349b892ec1834496975c8
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spelling	oai:doaj.org-article:7295af61d46349b892ec1834496975c82021-12-05T14:10:51ZOptimized LMS algorithm for system identification and noise cancellation0334-18602191-026X10.1515/jisys-2020-0081https://doaj.org/article/7295af61d46349b892ec1834496975c82021-02-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0081https://doaj.org/toc/0334-1860https://doaj.org/toc/2191-026XOptimization by definition is the action of making most effective or the best use of a resource or situation and that is required almost in every field of engineering. In this work, the optimization of Least Mean square (LMS) algorithm is carried out with the help of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Efforts have been made to find out the advantages and disadvantages of combining gradient based (LMS) algorithm with Swarm Intelligence SI (ACO, PSO). This optimization of LMS algorithm will help us in further extending the uses of adaptive filtering to the system having multi-model error surface that is still a gray area of adaptive filtering. Because the available version of LMS algorithm that plays an important role in adaptive filtering is a gradient based algorithm, that get stuck at the local minima of system with multi-model error surface considering it global minima, resulting in an non-optimized convergence. By virtue of the proposed method we have got a profound solution for the problem associated with system with multimodal error surface. The results depict significant improvements in the performance and displayed fast convergence rate, rather stucking at local minima. Both the SI techniques displayed their own advantage and can be separately combined with LMS algorithm for adaptive filtering. This optimization of LMS algorithm will further help to resolve serious interference and noise issues and holds a very important application in the field of biomedical science.Ling QianhuaIkbal Mohammad AsifKumar P.De Gruyterarticleant colony optimizationparticle swarm optimizationleast mean squarestep sizenoise cancellationsystem identificationScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 487-498 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ant colony optimization particle swarm optimization least mean square step size noise cancellation system identification Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	ant colony optimization particle swarm optimization least mean square step size noise cancellation system identification Science Q Electronic computers. Computer science QA75.5-76.95 Ling Qianhua Ikbal Mohammad Asif Kumar P. Optimized LMS algorithm for system identification and noise cancellation
description	Optimization by definition is the action of making most effective or the best use of a resource or situation and that is required almost in every field of engineering. In this work, the optimization of Least Mean square (LMS) algorithm is carried out with the help of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Efforts have been made to find out the advantages and disadvantages of combining gradient based (LMS) algorithm with Swarm Intelligence SI (ACO, PSO). This optimization of LMS algorithm will help us in further extending the uses of adaptive filtering to the system having multi-model error surface that is still a gray area of adaptive filtering. Because the available version of LMS algorithm that plays an important role in adaptive filtering is a gradient based algorithm, that get stuck at the local minima of system with multi-model error surface considering it global minima, resulting in an non-optimized convergence. By virtue of the proposed method we have got a profound solution for the problem associated with system with multimodal error surface. The results depict significant improvements in the performance and displayed fast convergence rate, rather stucking at local minima. Both the SI techniques displayed their own advantage and can be separately combined with LMS algorithm for adaptive filtering. This optimization of LMS algorithm will further help to resolve serious interference and noise issues and holds a very important application in the field of biomedical science.
format	article
author	Ling Qianhua Ikbal Mohammad Asif Kumar P.
author_facet	Ling Qianhua Ikbal Mohammad Asif Kumar P.
author_sort	Ling Qianhua
title	Optimized LMS algorithm for system identification and noise cancellation
title_short	Optimized LMS algorithm for system identification and noise cancellation
title_full	Optimized LMS algorithm for system identification and noise cancellation
title_fullStr	Optimized LMS algorithm for system identification and noise cancellation
title_full_unstemmed	Optimized LMS algorithm for system identification and noise cancellation
title_sort	optimized lms algorithm for system identification and noise cancellation
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/7295af61d46349b892ec1834496975c8
work_keys_str_mv	AT lingqianhua optimizedlmsalgorithmforsystemidentificationandnoisecancellation AT ikbalmohammadasif optimizedlmsalgorithmforsystemidentificationandnoisecancellation AT kumarp optimizedlmsalgorithmforsystemidentificationandnoisecancellation
_version_	1718371672570986496

Optimized LMS algorithm for system identification and noise cancellation

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