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This paper studies consensus seeking over noisy networks with time-varying noise statistics. Stochastic approximation type algorithms can ensure consensus in mean square and with probability one. For performance evaluation, we examine the long term behavior of the approximation error which consists of two naturally defined components. We show that the two components and their sum are each asymptotically...
We consider consensus seeking with measurement noise in directed graphs containing a spanning tree. By using stochastic approximation type algorithms, we show the state of each agent converges in mean square and almost surely to the same limit. Furthermore, we show that the approximation error, as the difference between the state vector and its limit, is asymptotically normal after normalization,...
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