Optimization of Type-2 Fuzzy Integration in Modular Neural Networks Using an Evolutionary Method with Applications in Multimodal Biometry

被引：0
作者：
Hidalgo, Denisse [1 ]
Melin, Patricia [2 ]
Licea, Guillermo [1 ]
Castillo, Oscar [2 ]
机构：
[1] UABC Univ, Sch Engn, Tijuana, Mexico
[2] Tijuana Inst Technol, Div Grad Studies, Tijuana, Mexico
来源：
MICAI 2009: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2009年 / 5845卷
关键词：
Modular Neural Network; Type-2 Fuzzy Logic; Genetic Algorithms; LOGIC;
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：
We describe in this paper a new evolutionary method for the optimization of a modular neural network for multimodal biometry The proposed evolutionary method produces the best architecture of the modular neural network (number of modules, layers and neurons) and fuzzy inference systems (memberships functions and rules) as fuzzy integration methods. The integration of responses in the modular neural network is performed by using type-1 and type-2 fuzzy inference systems.
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收藏
页码：454 / +
页数：4
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