Title :
Similarity measures in fuzzy rule base simplification
Author :
M. Setnes;R. Babuska;U. Kaymak;H.R. van Nauta Lemke
Author_Institution :
Dept. of Electr. Eng., Delft Univ. of Technol., Netherlands
Abstract :
In fuzzy rule-based models acquired from numerical data, redundancy may be present in the form of similar fuzzy sets that represent compatible concepts. This results in an unnecessarily complex and less transparent linguistic description of the system. By using a measure of similarity, a rule base simplification method is proposed that reduces the number of fuzzy sets in the model. Similar fuzzy sets are merged to create a common fuzzy set to replace them in the rule base. If the redundancy in the model is high, merging similar fuzzy sets might result in equal rules that also can be merged, thereby reducing the number of rules as well. The simplified rule base is computationally more efficient and linguistically more tractable. The approach has been successfully applied to fuzzy models of real world systems.
Keywords :
"Fuzzy sets","Fuzzy systems","Merging","Fuzzy neural networks","Automatic control","Numerical models","Nonlinear systems","Neural networks","Councils","Natural languages"
Journal_Title :
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
DOI :
10.1109/3477.678632