Hayden, D and Yuan, Y and Goncalves, J (2012) Robust network reconstruction in polynomial time. Proceedings of the IEEE Conference on Decision and Control. pp. 4616-4621. ISSN 0191-2216Full text not available from this repository.
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  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. © 2012 IEEE.
|Divisions:||Div F > Control|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:25|
|Last Modified:||27 Apr 2017 08:16|