Application of Interval Type-2 Fuzzy Logic for Estimating Module Relevance in Sugeno Integration of Modular Neural Networks

被引：0
|
作者：
Mendoza, Olivia [2 ]
Melin, Patricia [1 ]
Castillo, Oscar [1 ]
机构：
[1] Tijuana Inst Technol, Div Grad Studies, Tijuana, Mexico
[2] UABC Univ, Sch Engn, Tijuana, Mexico
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3 | 2009年
关键词：
FACE RECOGNITION;
D O I：
10.1109/FUZZY.2009.5277419
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 work we describe a Fuzzy Inference System to determine the relevance of each module in Modular Neural Networks for images recognition. The tests were made with Type I and Interval Type-2 Fuzzy Inference System, to compare the performance. In both cases the fusion operator for the modules is the Sugeno Integral, and the parameters to estimate are the fuzzy densities.
引用
收藏
页码：2120 / +
页数：2
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