Abstract
In complex manufacturing plants it is generally difficult to define first principle models to describe the system behaviour mathematically. In this paper we show how to generate data based models, whose input structure allows a physical interpretation.
Therefore we applied the transfer entropy method in order to find cause-effect relationships in a given or measured data set usually from industrial plants and multivariate correlation analysis to decrease the dimension of the basic problem of data based identification. One main problem of such data is the poor information which is sometimes contained in the data. We present results of the proposed method dealing with this problems by using data of real applications.
| Original language | English |
|---|---|
| Title of host publication | IFAC Workshop on Manufacturing Modelling, Management and Control |
| Number of pages | 5 |
| Publication status | Published - 2007 |
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
