Possibilistic Rules from Fuzzy Prototypes

Guanyi Li, Jonathan Lawry

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

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.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence
Subtitle of host publicationCommunications in Computer and Information Science
PublisherSpringer
Pages255-264
Publication statusPublished - 2012

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