Name
Abstract | ||
|---|---|---|
This paper considers the identification of Wiener systems in a worst case framework. Given some a priori information about the admissible set of plants, nonlinearities and measurement noise, and a posteriori experimental data, our goal is twofold: (i) establish whether the a priori and a posteriori information are consistent, and (ii) in that case find a model that interpolates the available experimental information within the noise level. As recently shown, this problem is generically NP hard both in the number of data points and the number of inputs to the non-linearity. Our main result shows that a computationally attractive relaxation can be obtained by recasting the problem as a rank-constrained semi-definite optimization and using existing tools specifically tailored to this type of problems. These results are illustrated with a practical application drawn from computer vision |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CDC.2011.6161114 | CDC-ECE |
Keywords | Field | DocType |
Wiener filters,computational complexity,convex programming,NP hard problem,Wiener systems,computer vision,convex relaxations,data points,experimental information,posteriori information,priori information,robust identification,semidefinite optimization,worst case framework | Data point,Data modeling,Mathematical optimization,Polynomial,Noise measurement,Computer science,Control theory,A priori and a posteriori,Admissible set,Convex optimization,Computational complexity theory | Conference |
ISSN | Citations | PageRank |
0743-1546 | 1 | 0.37 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Burak Yilmaz | 1 | 8 | 1.98 |
| Mustafa Ayazoglu | 2 | 36 | 3.91 |
| Mario Sznaier | 3 | 656 | 56.66 |
| Constantino M. Lagoa | 4 | 164 | 25.38 |