scholarly journals Gramian-Based Adaptive Combination Policies for Diffusion Learning Over Networks

Author(s):  
Y. Efe Erginbas ◽  
Stefan Vlaski ◽  
Ali H. Sayed
Keyword(s):  
2021 ◽  
Vol 11 (12) ◽  
pp. 5723
Author(s):  
Chundong Xu ◽  
Qinglin Li ◽  
Dongwen Ying

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.


2008 ◽  
Vol 2008 ◽  
pp. 1-11 ◽  
Author(s):  
F. Bajramovic ◽  
B. Deutsch ◽  
Ch. Gräßl ◽  
J. Denzler

2018 ◽  
Vol 69 ◽  
pp. 113-124 ◽  
Author(s):  
Juan Carlos Perafan Villota ◽  
Felipe Leno da Silva ◽  
Ricardo de Souza Jacomini ◽  
Anna Helena Reali Costa

Nature ◽  
2000 ◽  
Vol 407 (6805) ◽  
pp. 742-747 ◽  
Author(s):  
Kurt A. Thoroughman ◽  
Reza Shadmehr

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