@inproceedings{76e76c1a672843c7a8ade5b312705bfc,
title = "Decentralized parameter estimation by consensus based stochastic approximation",
abstract = "In this paper an algorithm for decentralized estimation of parameters in linear discrete-time regression models is proposed in the form of a combination of local stochastic approximation algorithms and a global consensus strategy. A rigorous analysis of the asymptotic properties of the proposed algorithm is presented, taking into account both the multi-agent network structure and the probabilities of local measurements and communication faults. In the case of non-vanishing gains in the stochastic approximation algorithms, an upper bound of the mean-square estimation error matrix is defined as a solution of a Lyapunov-like matrix equation, while in the case of asymptotically vanishing gains the mean-square convergence is proved. It is also demonstrated how the consensus strategy can contribute to the reduction of measurement noise influence.",
keywords = "Consensus strategy, Convergence analysis, Decentralized estimation, Denoising, Multi-agent systems, Stochastic approximation",
author = "Stankovi{\'c}, {Srdjan S.} and Stankovi{\'c}, {Milo{\v s} S.} and Stipanovi{\'c}, {Du{\v s}an M.}",
year = "2007",
doi = "10.1109/CDC.2007.4434812",
language = "English (US)",
isbn = "1424414989",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1535--1540",
booktitle = "Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC",
address = "United States",
note = "46th IEEE Conference on Decision and Control 2007, CDC ; Conference date: 12-12-2007 Through 14-12-2007",
}