Successive overrelaxation for Mamdani fuzzy systems

被引：0
|
作者：
Cai, Qianfeng [1 ,2 ]
Hao, Zhifeng [3 ]
Yang, Xiaowei [3 ]
机构：
[1] South China Univ Technol, Coll Comp Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China
[2] Guangdong Univ Technol, Coll Appl Math, Guangzhou, Guangdong, Peoples R China
[3] South China Univ Technol, Sch Math Sci, Guangzhou 510641, Guangdong, Peoples R China
来源：
FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 3, PROCEEDINGS | 2007年
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1109/FSKD.2007.546
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
To design a Mamdani fuzzy system with good generalization ability in high dimensional feature space, a novel learning algorithm based on the structural risk minimization (SRM) inductive principle is presented in this paper. Firstly, the parameter estimation of a Mamdani fuzzy system is converted to a quadratic optimization problem. Then, a versatile iterative method, successive overrelaxation, is proposed In the proposed algorithm, the fuzzy kernel generated by premise membership functions is proved to be a mercer kernel. Numerical experiments show that the presented algorithm improves the generalization ability of Mamdani fuzzy systems.
引用
收藏
页码：699 / +
页数：3
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