Abstract
It has been known that fuzzy system provides a framework to handle uncertainties and vagueness, but the applications often face difficulties in deciding the number of inference rules, either in Sugeno or Mamdani fuzzy rules, and input/output membership functions. A five-layer neuro-fuzzy model is developed in this work and its model accuracy is validated by a nonlinear model benchmark test. Analysis shows that both Sugeno and Mamdani neuro-fuzzy models have good performance in system identification, and the former achieves better accuracy.
| Original language | English |
|---|---|
| Pages (from-to) | 263-270 |
| Number of pages | 8 |
| Journal | Journal of Aeronautics, Astronautics and Aviation |
| Volume | 41 |
| Issue number | 4 |
| Publication status | Published - 2009 Dec |
All Science Journal Classification (ASJC) codes
- Aerospace Engineering
- Space and Planetary Science