Publication
We show how the convergence time of an adaptive network can be estimated in a distributed manner by the agents. Using this procedure, we propose a distributed mechanism for the nodes to switch from using fixed doubly-stochastic combination weights to adaptive combination weights. By doing so, and by knowing when to switch, the agents are able to enhance their steady-state mean-square-error performance without degrading the rate of convergence during the transient phase of the learning algorithm.
Alexandre Massoud Alahi, Yuejiang Liu
Daniel Kuhn, Yves Rychener, Tobias Sutter
Rachid Guerraoui, Nirupam Gupta, John Stephan, Sébastien Louis Alexandre Rouault, Rafaël Benjamin Pinot