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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Distributed estimation in diffusion networks using affine least-squares combiners

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Author(s):
Fernandez-Bes, Jesus [1] ; Azpicueta-Ruiz, Luis A. [1] ; Arenas-Garcia, Jeronimo [1] ; Silva, Magno T. M. [2]
Total Authors: 4
Affiliation:
[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
Total Affiliations: 2
Document type: Journal article
Source: DIGITAL SIGNAL PROCESSING; v. 36, p. 1-14, JAN 2015.
Web of Science Citations: 11
Abstract

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)

FAPESP's process: 13/18041-5 - Combination of adaptive algorithms and distributed adaptive filtering applied to acoustics
Grantee:Magno Teófilo Madeira da Silva
Support type: Research Grants - Visiting Researcher Grant - International
FAPESP's process: 12/24835-1 - Adaptive algorithms, combinations and applications in deconvolution
Grantee:Magno Teófilo Madeira da Silva
Support type: Regular Research Grants