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Genetic learning of fuzzy rule‐based classification systems cooperating with fuzzy reasoning methods
Author(s) -
Cordón Oscar,
José del Jesus María,
Herrera Francisco
Publication year - 1998
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(199810/11)13:10/11<1025::aid-int9>3.0.co;2-n
Subject(s) - artificial intelligence , fuzzy rule , computer science , fuzzy logic , neuro fuzzy , fuzzy set operations , set (abstract data type) , fuzzy classification , process (computing) , variable (mathematics) , machine learning , base (topology) , fuzzy set , defuzzification , type 2 fuzzy sets and systems , genetic algorithm , fuzzy control system , fuzzy number , mathematics , mathematical analysis , programming language , operating system
In this paper, we present a multistage genetic learning process for obtaining linguistic fuzzy rule‐based classification systems that integrates fuzzy reasoning methods cooperating with the fuzzy rule base and learns the best set of linguistic hedges for the linguistic variable terms. We show the application of the genetic learning process to two well known sample bases, and compare the results with those obtained from different learning algorithms. The results show the good behavior of the proposed method, which maintains the linguistic description of the fuzzy rules. © 1998 John Wiley & Sons, Inc.