Since the final membership function value,if obtained by extracting fuzzy rules on high dimension space(face,for instance),is not so reasonable and might very well be zero values,human adjustment is made on the initial value of the membership function during the extraction of fuzzy rules.

The method is studied in two aspects: decision rules acquisition and class forecast.

This paper studies the knowledge rules acquisition methodology based on Rough set for fault diagnosis.

Incremental rule acquisition method based on genetic algorithm and rough set theory;

The purpose of this study is to present a method of driving rule acquisition using variable precision rough set theory.

With the purpose of tackling problems such as uncertainty and mass rules of Knowledge in substation fuzzy expert systems,a effective method for knowledge denotation and rule acquisition is proposed by using colored FPN models.