Journal
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
Volume 32, Issue 4, Pages 526-537Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2002.1018771
Keywords
accuracy improvement; cooperative rules; linguistic fuzzy rule-based modeling; simulated annealing
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This paper introduces a new learning methodology to quickly generate accurate and simple linguistic fuzzy models, the cooperative rules (COR) methodology. It acts on the consequents of the fuzzy rules to find those best cooperating. Instead of selecting the consequent with the highest performance in each fuzzy input subspace as ad hoc data-driven methods usually do, the COR methodology considers the possibility of using another consequent, different from the best one, when it allows the fuzzy model to be more accurate thanks to having a rule set with best cooperation. Our proposal has shown good results solving three different applications when compared to other methods.
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