DocumentCode
2824965
Title
Stochastic approximation for consensus seeking: Mean square and almost sure convergence
Author
Huang, Minyi ; Manton, Jonathan H.
Author_Institution
Carleton Univ., Ottawa
fYear
2007
fDate
12-14 Dec. 2007
Firstpage
306
Lastpage
311
Abstract
We consider stochastic consensus problems in strongly connected directed graph models where each agent has noisy measurements of its neighbors´ states. For consensus seeking, we develop stochastic approximation type algorithms with a decreasing step size and establish mean square and almost sure convergence of the agents´ states to the same limit.
Keywords
convergence; directed graphs; mean square error methods; multi-agent systems; stochastic processes; almost sure convergence; consensus seeking; directed graph models; mean square; stochastic approximation; stochastic consensus problems; Approximation algorithms; Control systems; Convergence; Fading; Laplace equations; Quantization; Stochastic processes; Stochastic resonance; USA Councils; Wireless sensor networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 2007 46th IEEE Conference on
Conference_Location
New Orleans, LA
ISSN
0191-2216
Print_ISBN
978-1-4244-1497-0
Electronic_ISBN
0191-2216
Type
conf
DOI
10.1109/CDC.2007.4434630
Filename
4434630
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