Genetic tolerance fuzzy neural networks: From data to fuzzy hyperboxes

被引：4
|
作者：
Pedrycz, Witold [1 ]
机构：
[1] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB TG6 2G7, Canada
[2] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland
来源：
NEUROCOMPUTING | 2007年 / 70卷 / 7-9期
基金：
加拿大自然科学与工程研究理事会;
关键词：
computational intelligence; tolerance; dominance; inclusion; logic networks; tolerance neuron; fuzzy hyperboxes; genetic algorithms; fuzzy intervals;
D O I：
10.1016/j.neucom.2006.06.001
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 study, we introduce and discuss a category of genetically optimized fuzzy neural networks. As far as the underlying geometry of such networks is concerned, they are focused on revealing a hyperbox-based topology in numeric data. This class of the networks is developed around fuzzy tolerance neurons. Tolerance neurons form a generalized version of intervals (sets) arising in a form of fuzzy intervals. The architecture of the network reflects a hierarchy of geometric concepts typically exploited in data analysis: fuzzy intervals combined and-wise give rise to fuzzy hyperboxes and these in turn by being aggregated or-wise generate a summary of data as a collection of hyperboxes. We discuss a genetic form of optimization of the networks and provide an in-depth view into the geometry of the individual hyperboxes as well as the overall topology of the network. Numerical experiments deal with 2-D synthetic data. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码：1403 / 1413
页数：11
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