Name
Abstract | ||
|---|---|---|
State statistics of a linear system obey certain structural constraints that arise from the underlying dynamics and the directionality of input disturbances. Herein, we formulate completion problems of partially known state statistics with the added freedom of identifying disturbance dynamics. The goal of the proposed completion problem is to obtain information about input excitations that explain observed sample statistics. Our formulation aims at low-complexity models for admissible disturbances. The complexity represents the dimensionality of the subspace of the state-dynamics that is directly affected by disturbances. An example is provided to illustrate that colored-in-time stochastic processes can be effectively used to explain available data. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CDC.2013.6760127 | CDC |
Keywords | Field | DocType |
state statistics,low-complexity models,stochastic processes,matrix completion problem,nuclear norm regularization,matrix algebra,low-rank approximation,colored-in-time stochastic process,noise statistics,disturbance dynamics,structured matrix completion problems,convex optimization,state covariances | Mathematical optimization,Matrix completion,Linear system,Subspace topology,Computer science,Stochastic process,Curse of dimensionality,Low-rank approximation,State-transition matrix,Convex optimization | Conference |
ISSN | ISBN | Citations |
0743-1546 | 978-1-4673-5714-2 | 2 |
PageRank | References | Authors |
0.39 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongxin Chen | 1 | 99 | 31.89 |
| Mihailo R. Jovanovic | 2 | 594 | 55.52 |
| Tryphon T. Georgiou | 3 | 211 | 36.71 |