A method of improving performance of fuzzy neural network based on Differential Evolution

被引：1
|
作者：
Ma, Ming [1 ]
Xu, Yan [1 ]
Zhang, Li-Biao [2 ]
机构：
[1] Beihua Univ, Informat Manage Ctr, Jilin 132013, Peoples R China
[2] Jilin Univ, Coll Comp Sci &Technol, Changchun 130012, Peoples R China
来源：
PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2008年
关键词：
differential evoltion; fuzzy neural network; fuzzy rule;
D O I：
10.1109/ICMLC.2008.4620527
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Differential evolution is a powerful evolutionary inspired search technique for global optimization. We have proposed a new algorithm based on differential Evolution to solve the fuzzy neural network design problem, it can identify an optimal and efficient fuzzy, neural network structure for a given problem. Numerical simulations show the effectiveness of the proposed algorithm.
引用
收藏
页码：874 / +
页数：2
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