Rule base simplification with similarity measures
Robert Babuška, MAGNE SETNES, Uzay Kaymak, H.R. van Nauta Lemke · Proceedings of IEEE 5th International Fuzzy Systems · 2002
In fuzzy rule based models, redundancy may be present in the form of similar fuzzy sets, especially if the models are acquired from data by using techniques like fuzzy clustering or gradient learning. The result is an unnecessarily complex and a less effective linguistic description of the system. An automated method is proposed that reduces the number of fuzzy sets in the model using a similarity measure. A comprehensive linguistic description is obtained by linguistic approximation. A numerical example demonstrates the approach.