Abstract
A multi-agent based averaging consensus algorithm is investigated over noisy undirected connected graphs. It is assumed that each agent can know measurements of the neighbors' state with bounded errors. A rigorous stopping rule which reveals an explicit relation between the number of iterations and the closeness of the agreement with a probabilistic guarantee is established based on a matrix Bernstein inequality. The result is illustrated through a numerical example.
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