Strategies for Fuzzy Inference within Classifier Systems

Keeley A. Crockett, Zuhair Bandar, and David Mclean

When designing any type of fuzzy rule based system, considerable effort is placed in identifying the correct number of fuzzy sets and the fine tuning of the corresponding membership functions. Once a rule base has been formulated a fuzzy inference strategy must be applied in order to combine grades of membership. Considerable time and effort is spent trying to determine the number of fuzzy sets for a given system while substantially less time is invested in obtaining the most suitable inference strategy. This paper investigates a number of theoretical proven fuzzy inference strategies in order to assess the impact of these strategies on the performance of a fuzzy rule based classifier system. A fuzzy inference framework is proposed, which allows the investigation of five pure theoretical fuzzy inference operators in two real world applications. An additional two novel fuzzy-neural strategies are proposed and a comparative study is undertaken. The results show that the selection of the most suitable inference strategy for a given domain can lead to a significant improvement in performance.


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.