Design of Interval Type-2 Fuzzy Neural Networks and Their Optimization Using Real-coded Genetic Algorithms

被引：7
|
作者：
Park, Keon-Jun [1 ]
Oh, Sung-Kwun [1 ]
Pedrycz, Witold [2 ]
机构：
[1] Univ Suwon, Dept Elect Engn, San 2-2, Hwaseong Si 445743, Gyeonggi Do, South Korea
[2] Univ Alberta, Dept Elect & Comp Engn, Syst Res Inst, Polish Acad Sci, Edmonton, AB T6G 2G6, Canada
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3 | 2009年
关键词：
LOGIC SYSTEMS;
D O I：
10.1109/FUZZY.2009.5277365
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we introduce the design methodology of interval type-2 fuzzy neural networks (IT2FNN). And to optimize the network we use a real-coded genetic algorithm. IT2FNN is the network of combination between the fuzzy neural network (FNN) and interval type-2 fuzzy set with uncertainty. The antecedent part of the network is composed of the fuzzy division of input space and the consequence part of the network is represented by polynomial functions. The parameters such as the apexes of membership function, uncertainty parameter, the learning rate and the momentum coefficient are optimized using genetic algorithm (GA). The proposed network is evaluated with the performance between the approximation and the generalization abilities.
引用
收藏
页码：2013 / +
页数：2
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