COR:: A methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rules

被引:64
作者
Casillas, J [1 ]
Cordón, O [1 ]
Herrera, F [1 ]
机构
[1] Univ Granada, ETSI Informat, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain
来源
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2002年 / 32卷 / 04期
关键词
accuracy improvement; cooperative rules; linguistic fuzzy rule-based modeling; simulated annealing;
D O I
10.1109/TSMCB.2002.1018771
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:526 / 537
页数:12
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