Name
Abstract | ||
|---|---|---|
Set-membership identification of Hammerstein-Wiener models is addressed in the paper. First, it is shown that computation of tight parameter bounds requires the solutions to a number of nonconvex constrained polynomial optimization problems where the number of decision variables increases with the length of the experimental data sequence. Then, a suitable convex relaxation procedure is presented to significantly reduce the computational burden of the identification problem. A detailed discussion of the identification algorithm properties is reported. Finally, a simulated example is used to show the effectiveness and the computational tractability of the proposed approach. |
Year | Venue | Keywords |
|---|---|---|
2011 | 2011 50TH IEEE CONFERENCE ON DECISION AND CONTROL AND EUROPEAN CONTROL CONFERENCE (CDC-ECC) | uncertainty,decision support systems,noise,optimization,vectors,decision support system,parameter estimation,polynomials |
Field | DocType | ISSN |
Decision variables,Polynomial optimization,Mathematical optimization,Polynomial,Computer science,Decision support system,Estimation theory,Convex relaxation,Parameter identification problem,Computation | Conference | 0743-1546 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vito Cerone | 1 | 100 | 17.07 |
| Dario Piga | 2 | 94 | 16.53 |
| Diego Regruto | 3 | 174 | 22.43 |