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.
|Number of pages||8|
|Journal||Journal of Aeronautics, Astronautics and Aviation|
|Publication status||Published - 2009 Jan 1|
All Science Journal Classification (ASJC) codes
- Aerospace Engineering
- Space and Planetary Science