Name
Abstract | ||
|---|---|---|
This paper presents an efficient algorithm for robust network reconstruction of Linear Time-Invariant (LTI) systems in the presence of noise, estimation errors and unmodelled nonlinearities. The method here builds on previous work [1] on robust reconstruction to provide a practical implementation with polynomial computational complexity. Following the same experimental protocol, the algorithm obtains a set of structurally-related candidate solutions spanning every level of sparsity. We prove the existence of a magnitude bound on the noise, which if satisfied, guarantees that one of these structures is the correct solution. A problem-specific model-selection procedure then selects a single solution from this set and provides a measure of confidence in that solution. Extensive simulations quantify the expected performance for different levels of noise and show that significantly more noise can be tolerated in comparison to the original method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CDC.2012.6426135 | Decision and Control |
Keywords | Field | DocType |
computational complexity,estimation theory,polynomials,robust control,estimation errors,experimental protocol,linear time invariant system,magnitude bound,polynomial computational complexity,polynomial time,problem specific model selection procedure,robust network reconstruction,robust reconstruction,sparsity,structurally related candidate solutions spanning,unmodelled nonlinearities | Magnitude (mathematics),Mathematical optimization,Polynomial,Control theory,Computer science,Estimation theory,Robust control,Time complexity,Computational complexity theory | Conference |
ISSN | ISBN | Citations |
0743-1546 E-ISBN : 978-1-4673-2064-1 | 978-1-4673-2064-1 | 1 |
PageRank | References | Authors |
0.44 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David P. Hayden | 1 | 1 | 0.44 |
| Ye Yuan | 2 | 38 | 4.92 |
| Jorge M. Goncalves | 3 | 1 | 0.44 |