Learning a fuzzy rule based knowledge representation

This paper presents an automatic learning algorithm which generates a fuzzy rule based knowledge representation. While learning the membership functions and rules the internal structure of the rule base is also considered. This is done by definition of 1 a complexity cost function and 2 a minimal Fuzzy System. A Genetic Algorithm is used to estimate the Fuzzy Systems which capture a low comlex and minimal rule base. Optimization of the “entropy” of a fuzzy rule base leads to a minimal number of rules, of membership functions and of subpremises together with an optimal input/output behavior. The resulting Fuzzy System is comparable to systems designed by an expert but with a better performance. The approach is compared to others by a standard benchmark (a system identification process) and different results for symmetric and non-symmetric membership functions are presented.