Abstract
In this paper superheating system of a 325MW steam power plant is modeled based on the recurrent neuro-fuzzy networks and subtractive clustering. The experimental data are obtained from a complete set of field experiments under various operating conditions. Nine neuro-fuzzy models are constructed and trained for seven subsystems of the superheating unit. Then, these nine fuzzy models are put together merging series and parallel units according to the real power plant subsystems, to obtain the global model of the superheating process. Comparing the time response of the nonlinear neuro-fuzzy model of a subsystem with the time response of its linear model based on the Least Square Error (LSE) method, indicates that the nonlinear neurofuzzy model is more accurate and reliable than the linear model in the sense that its response is closer to the response of the actual superheating system.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2006 |
| Pages | 347-352 |
| Number of pages | 6 |
| Publication status | Published - 2006 |
| Event | IASTED International Conference on Artificial Intelligence and Applications, AIA 2006 - Innsbruck, Austria Duration: Feb 13 2006 → Feb 16 2006 |
Other
| Other | IASTED International Conference on Artificial Intelligence and Applications, AIA 2006 |
|---|---|
| Country | Austria |
| City | Innsbruck |
| Period | 2/13/06 → 2/16/06 |
Keywords
- Fuzzy sets
- Neuro-fuzzy systems
- Nonlinear modeling
- Nonlinear systems
- PID controller
- Steam power plant
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Software