Name
Abstract | ||
|---|---|---|
The problem of modeling a linear dynamic system is discussed and a novel approach to automatically combine black-box and white-box models is introduced. The solution proposed in this contribution is based on the usage of regularized finite-impulse-response (FIR) models. In contrast to classical gray-box modelling, which often only optimizes the parameters of a given model structure, our approach is able to handle the problem of undermodeling as well. Therefore, the amount of trust in the whitebox or gray-box model is optimized based on a generalized cross-validation criterion. The feasibility of the approach is demonstrated with a pendulum example. It is furthermore investigated, which level of prior knowledge is best suited for the identification of the process. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1515/auto-2018-0026 | AT-AUTOMATISIERUNGSTECHNIK |
Keywords | Field | DocType |
system identification,Bayesian methods,FIR system,gray-box modelling | Control theory,Algorithm,Gray box testing,Engineering | Journal |
Volume | Issue | ISSN |
66 | 9 | 0178-2312 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tobias Munker | 1 | 0 | 1.01 |
| Timm J. Peter | 2 | 0 | 0.68 |
| Oliver Nelles | 3 | 99 | 17.27 |