Learning fuzzy information in a hybrid connectionist, symbolic model
Steve G. Romaniuk, Lawrence Hall · 2003
An implementation of fuzzy variables using pi-shaped membership functions is shown in a hybrid symbolic connectionist expert system tool that uses fuzzy logic to implement reasoning with uncertainty and imprecision and that can learn from imprecise data. A method of dynamically modifying the arms, or fuzzy part of the membership functions, during learning is shown. Examples illustrating the method are presented. The results indicate that the presented system is capable of learning membership functions for applications such as control or classification.>