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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Distributed estimation in diffusion networks using affine least-squares combiners

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Autor(es):
Fernandez-Bes, Jesus [1] ; Azpicueta-Ruiz, Luis A. [1] ; Arenas-Garcia, Jeronimo [1] ; Silva, Magno T. M. [2]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Univ Carlos III Madrid, Dept Signal Theory & Commun, Leganes 28911 - Spain
[2] Univ Sao Paulo, Escola Politecn, Dept Elect Syst Engn, BR-05508010 Sao Paulo - Brazil
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: DIGITAL SIGNAL PROCESSING; v. 36, p. 1-14, JAN 2015.
Citações Web of Science: 11
Resumo

We propose a diffusion scheme for adaptive networks, where each node obtains an estimate of a common unknown parameter vector by combining a local estimate with the combined estimates received from neighboring nodes. The combination weights are adapted in order to minimize the mean-square error of the network employing a local least-squares (LS) cost function. This adaptive diffusion network with LS combiners (ADN-LS) is analyzed, deriving expressions for its network mean-square deviation that characterize the convergence and steady-state performance of the algorithm. Experiments carried out in stationary and tracking scenarios show that our proposal outperforms a state-of-art scheme for adapting the weights of diffusion networks (ACW algorithm from {[}10], both during convergence and in tracking situations. Despite its good convergence behavior, our proposal may present a slightly worse steady-state performance in stationary or slowly-changing scenarios with respect to ACW due to the error inherent to the least-squares adaptation with sliding window. Therefore, to take advantage of these different behaviors, we also propose a hybrid scheme based on a convex combination of the ADN-LS and ACW algorithms. (C) 2014 Elsevier Inc. All rights reserved. (AU)

Processo FAPESP: 12/24835-1 - Algoritmos adaptativos, combinações e aplicações em desconvolução
Beneficiário:Magno Teófilo Madeira da Silva
Linha de fomento: Auxílio à Pesquisa - Regular
Processo FAPESP: 13/18041-5 - Combinação de algoritmos adaptativos e filtragem adaptativa distribuída aplicadas à acústica
Beneficiário:Magno Teófilo Madeira da Silva
Linha de fomento: Auxílio à Pesquisa - Pesquisador Visitante - Internacional