Name
Abstract | ||
|---|---|---|
We present a subspace system identification method based on weighted nuclear norm approximation. The weight matrices used in the nuclear norm minimization are the same weights as used in standard subspace identification methods. We show that the inclusion of the weights improves the performance in terms of fit on validation data. Experimental results from randomly generated examples as well as from the Daisy benchmark collection are reported. The key to an efficient implementation is the use of the alternating direction method of multipliers to solve the optimization problem. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CDC.2012.6426980 | Decision and Control |
Keywords | Field | DocType |
approximation theory,identification,matrix algebra,optimisation,Daisy benchmark collection,alternating direction method,subspace system identification method,weight matrices,weighted nuclear norm approximation,weighted nuclear norm optimization | Mathematical optimization,Nuclear norm minimization,Subspace topology,Matrix (mathematics),Matrix algebra,Computer science,Approximation theory,Matrix norm,System identification,Optimization problem | Conference |
Volume | ISSN | ISBN |
abs/1207.0023 | 0743-1546 E-ISBN : 978-1-4673-2064-1 | 978-1-4673-2064-1 |
Citations | PageRank | References |
18 | 0.93 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anders Hansson | 1 | 32 | 3.26 |
| Zhang Liu | 2 | 18 | 0.93 |
| Lieven Vandenberghe | 3 | 18 | 0.93 |