Abstract
Fuzzy identification methods have been successfully applied to systems where nonlinearity is one of the main constraint feature. However noise inference of the fuzzy identification has been discussed in detail in such studies. In this paper we propose an efficient fuzzy identification method which has two strong points. The one is how to reduce the number of implication to construct a better fuzzy model. The other is to remove the noise effects when the systems are embedded in noise. By using this method we demonstrate a better fuzzy modeling result for such systems.
