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Fuzzy rule extraction by bacterial memetic algorithms
Author(s) -
Botzheim J.,
Cabrita C.,
Kóczy L. T.,
Ruano A. E.
Publication year - 2009
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/int.20338
Subject(s) - memetic algorithm , fuzzy rule , evolutionary algorithm , computer science , fuzzy logic , memetics , artificial intelligence , fuzzy set , algorithm , machine learning , mathematical optimization , mathematics
In our previous papers, fuzzy model identification methods were discussed. The bacterial evolutionary algorithm for extracting fuzzy rule base from a training set was presented. The Levenberg–Marquardt method was also proposed for determining membership functions in fuzzy systems. The combination of the evolutionary and the gradient‐based learning techniques is usually called memetic algorithm. In this paper, a new kind of memetic algorithm, the bacterial memetic algorithm, is introduced for fuzzy rule extraction. The paper presents how the bacterial evolutionary algorithm can be improved with the Levenberg–Marquardt technique. © 2009 Wiley Periodicals, Inc.