A new interpretation of description labels in fuzzy rules is introduced based on the idea of fuzzy prototypes. For each label L i in a description set we identify a fuzzy prototype resulting in a possibility distribution quantifying the possibility that L i is the most appropriate label to describe a given input value. A rule induction algorithm is then proposed for learning Takagi-Sugeno style rules from data, within this label representation framework. Given an appropriate inference and defuzzification method we then demonstrate the potential of this approach by its application to a number of benchmark regression problems.
|Title of host publication||Advances in Computational Intelligence|
|Subtitle of host publication||Communications in Computer and Information Science|
|Publication status||Published - 2012|