Multi-objective Fuzzy Modeling Using NSGA-II

被引：0
|
作者：
Xing Zong-Yi [1 ]
Zhang Yong [1 ]
Hou Yuan-Long [1 ]
Cai Guo-Qiang [2 ]
机构：
[1] Nanjing Univ Sci & Technol, Sch Mech Engn, Nanjing, Jiangsu, Peoples R China
[2] Beijing Jiaotong Univ, Sch Traffic & Transportat, Beijing, Peoples R China
来源：
2008 IEEE CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, VOLS 1 AND 2 | 2008年
基金：
美国国家科学基金会;
关键词：
Fuzzy modeling; fuzzy system; multi-objective genetic algorithm; Pareto-optimal; interpretability; NSGA-II;
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：
An approach to construct multiple Pareto-optimal fuzzy systems based on NSGA-II is proposed in this paper. First, in order to obtain a good initial fuzzy system, a modified fuzzy clustering algorithm is used to identify the antecedents of fuzzy system, while the consequents are designed separately to reduce computational burden. Second, a Pareto multi-objective genetic algorithm based on NSGA-II and the interpretability-driven simplification techniques are used to evolve the initial fuzzy system iteratively with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets. Resultantly, multiple Pareto-optimal fuzzy systems are obtained. The proposed approach is applied to two benchmark problems, and the results show its validity.
引用
收藏
页码：413 / +
页数：3
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