Defuzzification, structure transparency, and fuzzy system learning

Abstract

The issue of defuzzification is explored in the context of fuzzy system structure and learning for non-linear system modeling. It is revealed that the best-known defuzzification methods may not necessarily result in transparent fuzzy system structures that are universally approximating and yet suitable for developing effective learning algorithms for modeling. This paper then presents a simple defuzzification method that leads to transparent fuzzy system structures based on the min-max operations.