Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks

The incremental least-mean-square (ILMS) algorithm is a useful method to perform distributed adaptation and learning in Hamiltonian networks. To implement the ILMS algorithm, each node needs to receive the local estimate of the previous node on the cycle path to update its own local estimate. Howeve...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Azam Khalili, Vahid Vahidpour, Amir Rastegarnia, Ali Farzamnia, Kenneth Teo Tze Kin, Saeid Sanei
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	adaptive estimation coordinate-descent distributed networks incremental algorithm Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/bf739746f80e4649b03e778d174876a0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:bf739746f80e4649b03e778d174876a0
record_format	dspace
spelling	oai:doaj.org-article:bf739746f80e4649b03e778d174876a02021-11-25T18:58:51ZCoordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks10.3390/s212277321424-8220https://doaj.org/article/bf739746f80e4649b03e778d174876a02021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7732https://doaj.org/toc/1424-8220The incremental least-mean-square (ILMS) algorithm is a useful method to perform distributed adaptation and learning in Hamiltonian networks. To implement the ILMS algorithm, each node needs to receive the local estimate of the previous node on the cycle path to update its own local estimate. However, in some practical situations, perfect data exchange may not be possible among the nodes. In this paper, we develop a new version of ILMS algorithm, wherein in its adaptation step, only a random subset of the coordinates of update vector is available. We draw a comparison between the proposed coordinate-descent incremental LMS (CD-ILMS) algorithm and the ILMS algorithm in terms of convergence rate and computational complexity. Employing the energy conservation relation approach, we derive closed-form expressions to describe the learning curves in terms of excess mean-square-error (EMSE) and mean-square deviation (MSD). We show that, the CD-ILMS algorithm has the same steady-state error performance compared with the ILMS algorithm. However, the CD-ILMS algorithm has a faster convergence rate. Numerical examples are given to verify the efficiency of the CD-ILMS algorithm and the accuracy of theoretical analysis.Azam KhaliliVahid VahidpourAmir RastegarniaAli FarzamniaKenneth Teo Tze KinSaeid SaneiMDPI AGarticleadaptive estimationcoordinate-descentdistributed networksincremental algorithmChemical technologyTP1-1185ENSensors, Vol 21, Iss 7732, p 7732 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	adaptive estimation coordinate-descent distributed networks incremental algorithm Chemical technology TP1-1185
spellingShingle	adaptive estimation coordinate-descent distributed networks incremental algorithm Chemical technology TP1-1185 Azam Khalili Vahid Vahidpour Amir Rastegarnia Ali Farzamnia Kenneth Teo Tze Kin Saeid Sanei Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks
description	The incremental least-mean-square (ILMS) algorithm is a useful method to perform distributed adaptation and learning in Hamiltonian networks. To implement the ILMS algorithm, each node needs to receive the local estimate of the previous node on the cycle path to update its own local estimate. However, in some practical situations, perfect data exchange may not be possible among the nodes. In this paper, we develop a new version of ILMS algorithm, wherein in its adaptation step, only a random subset of the coordinates of update vector is available. We draw a comparison between the proposed coordinate-descent incremental LMS (CD-ILMS) algorithm and the ILMS algorithm in terms of convergence rate and computational complexity. Employing the energy conservation relation approach, we derive closed-form expressions to describe the learning curves in terms of excess mean-square-error (EMSE) and mean-square deviation (MSD). We show that, the CD-ILMS algorithm has the same steady-state error performance compared with the ILMS algorithm. However, the CD-ILMS algorithm has a faster convergence rate. Numerical examples are given to verify the efficiency of the CD-ILMS algorithm and the accuracy of theoretical analysis.
format	article
author	Azam Khalili Vahid Vahidpour Amir Rastegarnia Ali Farzamnia Kenneth Teo Tze Kin Saeid Sanei
author_facet	Azam Khalili Vahid Vahidpour Amir Rastegarnia Ali Farzamnia Kenneth Teo Tze Kin Saeid Sanei
author_sort	Azam Khalili
title	Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks
title_short	Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks
title_full	Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks
title_fullStr	Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks
title_full_unstemmed	Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks
title_sort	coordinate-descent adaptation over hamiltonian multi-agent networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bf739746f80e4649b03e778d174876a0
work_keys_str_mv	AT azamkhalili coordinatedescentadaptationoverhamiltonianmultiagentnetworks AT vahidvahidpour coordinatedescentadaptationoverhamiltonianmultiagentnetworks AT amirrastegarnia coordinatedescentadaptationoverhamiltonianmultiagentnetworks AT alifarzamnia coordinatedescentadaptationoverhamiltonianmultiagentnetworks AT kennethteotzekin coordinatedescentadaptationoverhamiltonianmultiagentnetworks AT saeidsanei coordinatedescentadaptationoverhamiltonianmultiagentnetworks
_version_	1718410487135207424

Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks

Ejemplares similares