Name
Abstract | ||
|---|---|---|
It has been recently shown in Ren et al. (2010) that by collecting noise-contaminated time series generated by a coupled-oscillator system at each node of a network, it is possible to robustly reconstruct its topology, i.e. determine the graph Laplacian. Restricting ourselves to linear consensus dynamics over undirected communication networks, in this paper we introduce a new dynamic average consensus least-squares algorithm to locally estimate these time series at each node, thus making the reconstruction process fully distributed and more easily applicable in the real world. We also propose a novel efficient method for separating the off-diagonal entries of the reconstructed Laplacian, and examine several concepts related to the trace of the dynamic correlation matrix of the coupled single integrators, which is a distinctive element of our network reconstruction method. The theory is illustrated with examples from computer, power and transportation systems. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.sysconle.2014.05.008 | Systems & Control Letters |
Keywords | Field | DocType |
Network reconstruction,Consensus algorithms,Distributed estimation,Networked systems | Laplacian matrix,Average consensus,Mathematical optimization,Reconstruction problem,Telecommunications network,Control theory,Computer science,Integrator,Theoretical computer science,Covariance matrix,Consensus dynamics,Laplace operator | Journal |
Volume | ISSN | Citations |
70 | 0167-6911 | 4 |
PageRank | References | Authors |
0.50 | 12 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabio Morbidi | 1 | 9 | 1.97 |
| Alain Y. Kibangou | 2 | 95 | 12.01 |