Decentralized parameter estimation by consensus based stochastic approximation
2011 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, Vol. 56, no 3, 531-543 p.Article in journal (Refereed) Published
In this paper, an algorithm for decentralized multi-agent 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. An analysis of the asymptotic properties of the proposed algorithm is presented, taking into account both the multi-agent network structure and the probabilities of getting local measurements and implementing exchange of inter-agent messages. In the case of non-vanishing gains in the stochastic approximation algorithms, an asymptotic estimation error covariance matrix bound is defined as the solution of a Lyapunov-like matrix equation. In the case of asymptotically vanishing gains, the mean-square convergence is proved and the rate of convergence estimated. In the discussion, the problem of additive communication noise is treated in a methodologically consistent way. It is also demonstrated how the consensus scheme in the algorithm can contribute to the overall reduction of measurement noise influence. Some simulation results illustrate the obtained theoretical results.
Place, publisher, year, edition, pages
2011. Vol. 56, no 3, 531-543 p.
Consensus, convergence, multi-agent systems, parameter estimation, sensor networks, stochastic approximation, Asymptotic estimation, Asymptotic properties, Communication noise, Consensus schemes, Consensus strategy, Discrete-time, Local measurement, Lyapunov, Matrix equations, Mean-square, Measurement Noise, Multi-Agent, Multiagent networks, Rate of convergence, Regression model, Simulation result, Stochastic approximation algorithms, Stochastic approximations, Theoretical result, Approximation algorithms, Approximation theory, Communication, Convergence of numerical methods, Covariance matrix, Intelligent agents, Metal analysis, Multi agent systems, Regression analysis, Stochastic models, Stochastic systems
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-151435DOI: 10.1109/TAC.2010.2076530ISI: 000289211100005ScopusID: 2-s2.0-79952607553OAI: oai:DiVA.org:kth-151435DiVA: diva2:748851
QC 201409222014-09-222014-09-222015-10-06Bibliographically approved