Abstract
This work presents a black-box input selection approach to reveal causal dependencies between process variables of complex industrial systems. This allows data based modeling with physically interpretable model structure.
For this purpose a method is used which combines statistical and analytical approaches to find causal relations between measured data, detection of control loops and the interaction of conditional system behavior respectively. The quality of such models remains in comparison to a common statistical approach unchanged high. The benefit of this input identification approach is an improved insight in complex processes for modeling purposes and their applications.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 11th WSEAS International Conference on Automatic Control, Modelling and Simulation |
| Number of pages | 7 |
| Publication status | Published - Jun 2009 |
Fields of science
- 202 Electrical Engineering, Electronics, Information Engineering
- 202027 Mechatronics
- 202034 Control engineering
- 203027 Internal combustion engines
- 206001 Biomedical engineering
- 206002 Electro-medical engineering
- 207109 Pollutant emission