Knowledge Discovery in Membership Functions in Fuzzy Modeling
نویسندگان
چکیده
During the procedure of fuzzy modeling, we use various methods to update the parameters of membership functions and the shapes of MFs change during the training procedure. So it is obvious that the shape and the distribution of MFs (not a single MF) must implicate some kinds of knowledge of target function (the function to fit) or the problem we try to model. This paper introduced the entropy E to describe the knowledge discovery of MFs. Then we use this kind of knowledge to explain and qualitatively predict the change of parameters of MFs during the training procedure. By implementing this, we may keep the shapes of MFs and our model comprehensible. Finally, we can induct the shapes of MFs correctly in the framework of comprehensibility and improve our fitting.
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