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MOGUL: A methodology to obtain genetic fuzzy rule‐based systems under the iterative rule learning approach
Author(s) -
Cordón O.,
del Jesus M. J.,
Herrera F.,
Lozano M.
Publication year - 1999
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(199911)14:11<1123::aid-int4>3.0.co;2-6
Subject(s) - fuzzy rule , computer science , rule based system , artificial intelligence , neuro fuzzy , fuzzy logic , process (computing) , fuzzy control system , fuzzy set operations , genetic algorithm , machine learning , operating system
The main aim of this paper is to present MOGUL , a Methodology to Obtain Genetic fuzzy rule‐based systems Under the iterative rule Learning approach. MOGUL will consist of some design guidelines that allow us to obtain different genetic fuzzy rule‐based systems, i.e., evolutionary algorithm‐based processes to automatically design fuzzy rule‐based systems by learning and/or tuning the fuzzy rule base, following the same generic structure and able to cope with problems of a different nature. A specific evolutionary learning process obtained from the paradigm proposed to design unconstrained approximate Mamdani‐type fuzzy rule‐based systems will be introduced, and its accuracy in the solving of a real‐world electrical engineering problem will be analyzed. ©1999 John Wiley & Sons, Inc.