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dc.contributor.author
Sui, Tianju
dc.contributor.author
Marelli, Damian Edgardo
dc.contributor.author
Fu, Minyue
dc.contributor.author
Lu, Renquan
dc.date.available
2019-11-25T22:14:49Z
dc.date.issued
2018-11
dc.identifier.citation
Sui, Tianju; Marelli, Damian Edgardo; Fu, Minyue; Lu, Renquan; Accuracy analysis for distributed weighted least-squares estimation in finite steps and loopy networks; Pergamon-Elsevier Science Ltd; Automatica; 97; 11-2018; 82-91
dc.identifier.issn
0005-1098
dc.identifier.uri
http://hdl.handle.net/11336/90395
dc.description.abstract
Distributed parameter estimation for large-scale systems is an active research problem. The goal is to derive a distributed algorithm in which each agent obtains a local estimate of its own subset of the global parameter vector, based on local measurements as well as information received from its neighbors. A recent algorithm has been proposed, which yields the optimal solution (i.e., the one that would be obtained using a centralized method) in finite time, provided the communication network forms an acyclic graph. If instead, the graph is cyclic, the only available alternative algorithm, which is based on iterative matrix inversion, achieving the optimal solution, does so asymptotically. However, it is also known that, in the cyclic case, the algorithm designed for acyclic graphs produces a solution which, although non optimal, is highly accurate. In this paper we do a theoretical study of the accuracy of this algorithm, in communication networks forming cyclic graphs. To this end, we provide bounds for the sub-optimality of the estimation error and the estimation error covariance, for a class of systems whose topological sparsity and signal-to-noise ratio satisfy certain condition. Our results show that, at each node, the accuracy improves exponentially with the so-called loop-free depth. Also, although the algorithm no longer converges in finite time in the case of cyclic graphs, simulation results show that the convergence is significantly faster than that of methods based on iterative matrix inversion. Our results suggest that, depending on the loop-free depth, the studied algorithm may be the preferred option even in applications with cyclic communication graphs.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Pergamon-Elsevier Science Ltd
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
CONVERGENCE RATE
dc.subject
DISTRIBUTED STATISTICAL ESTIMATION
dc.subject
WEIGHTED LEAST SQUARES
dc.subject.classification
Control Automático y Robótica
dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Accuracy analysis for distributed weighted least-squares estimation in finite steps and loopy networks
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-10-17T14:56:05Z
dc.journal.volume
97
dc.journal.pagination
82-91
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Sui, Tianju. Dalian University of Technology; China
dc.description.fil
Fil: Marelli, Damian Edgardo. Guandong University of Technology; China. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
dc.description.fil
Fil: Fu, Minyue. Guandong University of Technology; China. Universidad de Newcastle; Australia
dc.description.fil
Fil: Lu, Renquan. Guandong University of Technology; China
dc.journal.title
Automatica
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.automatica.2018.07.016
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S000510981830373X
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