ISSN : 1796-203X
Volume : 4    Issue : 2    Date : February 2009

A Genetic Fuzzy System Based On Improved Fuzzy Functions
Asli Celikyilmaz and I. Burhan Turksen
Page(s): 135-146
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Fuzzy inference systems based on fuzzy rule bases (FRBs) have been successfully used to model
real problems. Some of the limitations exhibited by these traditional fuzzy inference systems are that
there is an abundance of fuzzy operations and operators that an expert should identify. In this paper
we present an alternate learning and reasoning schema, which use fuzzy functions instead of
if…then rule base structures. The new fuzzy function approach optimized with genetic algorithms is
proposed to replace the fuzzy operators and operations of FRBs and improve accuracy of the fuzzy
models. The structure identification of the new approach is based on a supervised hybrid fuzzy
clustering, entitled Improved Fuzzy Clustering (IFC) method, which yields improved membership
values. The merit of the proposed fuzzy functions method is that the uncertain information on natural
grouping of data samples, i.e., membership values, is utilized as additional predictors while
structuring fuzzy functions and optimized with evolutionary methods. The comparative experiments
using real manufacturing and financial datasets demonstrate that the proposed method is
comparable or better in modeling systems of regression problem domains.

Index Terms
fuzzy functions, genetic algorithms, fuzzy clustering