This article describes the process of multicriteria optimization of a complex industrial control object using Pareto efficiency. The object is being decomposed and viewed as a hierarchy of embedded orgraphs. Performance indicators and controlling factors lists are created based on the orgraphs and technical specifications of an object, thus allowing to systematize sources of influence. Using statistical data archives to train, the neural network approximates key sensors data to identify the model of the controllable object and optimize it.
| Original language | English |
|---|---|
| Title of host publication | Data Mining and Big Data - 5th International Conference, DMBD 2020, Proceedings |
| Editors | Ying Tan, Yuhui Shi, Milan Tuba |
| Publisher | Springer Nature |
| Pages | 26-33 |
| Number of pages | 8 |
| ISBN (Print) | 9789811572043 |
| DOIs | |
| State | Published - 2020 |
| Externally published | Yes |
| Event | 5th International Conference on Data Mining and Big Data, DMBD 2020 - Belgrade, Serbia Duration: 14 Jul 2020 → 20 Jul 2020 |
| Name | Communications in Computer and Information Science |
|---|---|
| Volume | 1234 CCIS |
| ISSN (Print) | 1865-0929 |
| ISSN (Electronic) | 1865-0937 |
| Conference | 5th International Conference on Data Mining and Big Data, DMBD 2020 |
|---|---|
| Country/Territory | Serbia |
| City | Belgrade |
| Period | 14/07/20 → 20/07/20 |
ID: 77973602