Diagnostic Knowledge Extraction Based on Variable Precision Rough-Fuzzy Sets Integration Model

A method of diagnostic knowledge acquisition is proposed based on an integration model of variable precision rough sets and fuzzy sets.The method transforms the continuous attribute values into the fuzzy values by automatically deriving membership functions from a set of data with similarity clustering.With the concepts of fuzzy similarity relation and fuzzy similarity classes,lower and upper approximations of rough-fuzzy approximation space are given.Also,ant colony algorithm is introduced for attribute reduction,furthermore,diagnostic knowledge is obtained.The application to knowledge acquisition of pure terephthalic acid(PTA) oxidation process shows the proposed algorithm can find more objective and effective diagnostic rules from the quantitative data and is a good method in applications to fault diagnosis.