Title :
A novel scheme for diffusion networks with least-squares adaptive combiners
Author :
Fernández-Bes, Jesús ; Azpicueta-Ruiz, Luis A. ; Silva, Magno T M ; Arenas-García, Jerónimo
Author_Institution :
Univ. Carlos III de Madrid, Leganés, Spain
Abstract :
In this paper, we propose a novel diffusion scheme for adaptive networks, where each node preserves a pure local estimate of the unknown parameter vector and combines this estimate with other estimates received from neighboring nodes. The combination weights are adapted to minimize a local least-squares cost function. Simulations carried out in stationary and nonstationary scenarios show that the proposed scheme can outperform other existing schemes for diffusion networks with adaptive combiners in terms of tracking capability and convergence rate when the network nodes use different step sizes.
Keywords :
adaptive filters; convergence; least squares approximations; tracking; adaptive networks; combination weights; convergence rate; diffusion networks; least-squares adaptive combiners; local least-squares cost function; neighboring nodes; network nodes; nonstationary scenarios; pure local estimate; tracking capability; unknown parameter vector; Decision support systems; Global Positioning System; Mercury (metals); Adaptive filtering; adaptive networks; affine combination; diffusion; least squares;
Conference_Titel :
Machine Learning for Signal Processing (MLSP), 2012 IEEE International Workshop on
Conference_Location :
Santander
Print_ISBN :
978-1-4673-1024-6
Electronic_ISBN :
1551-2541
DOI :
10.1109/MLSP.2012.6349767