A fuzzy clustering based approach for generating interpretable fuzzy models

被引：0
|
作者：
Xing, ZY [1 ]
Hu, WL [1 ]
Jia, LM [1 ]
机构：
[1] Nanjing Univ Sci & Technol, Automat Dept, Nanjing 210094, Jiangsu, Peoples R China
来源：
PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2004年
关键词：
fuzzy modeling; fuzzy clustering; rule reduction; interpretability; genetic algorithm;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A systematic fuzzy modeling approach considering both accuracy and interpretability is developed in this paper. First, fuzzy clustering algorithm, combined with least square method, is used to obtain initial fuzzy model with excessive rules. Then rule reduction is performed by orthogonal least squares to obtain simplified fuzzy model with high interpretability. Finally, genetic algorithm is adopted to optimize the simplified fuzzy-model to improve accuracy. The proposed approach is successfully applied to a real world coke-oven temperature system, and the result shows its effectiveness.
引用
收藏
页码：2093 / 2097
页数：5
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