Evolutionary Learning for Neuro-fuzzy Ensembles with Generalized Parametric Triangular Norms

被引：0
|
作者：
Gabryel, Marcin [1 ,2 ]
Korytkowski, Marcin [1 ,2 ]
Pokropinska, Agata [4 ]
Scherer, Rafal [1 ,5 ]
Drozda, Stanislaw [3 ]
机构：
[1] Czestochowa Tech Univ, Dept Comp Engn, Al Armii Krajowej 36, PL-42200 Czestochowa, Poland
[2] Kotarbinski Olsztyn Acad Comp Sci & Management, PL-10165 Olsztyn, Poland
[3] Univ Warmia & Mazury, Fac Math & Comp Sci, PL-10561 Olsztyn, Poland
[4] Jan Dlugosz Univ, Inst Math & Comp Sci, Czestochowa, Poland
[5] Acad Management SWSPiZ, Inst Informat Technol, PL-90113 Lodz, Poland
来源：
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT I | 2010年 / 6113卷
关键词：
SYSTEMS;
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：
In this paper we present a method for designing neuro-fuzzy systems with Mamdani-type inference and parametric t-norm connecting rule antecedents. Hamacher product was used as t-norm. The neuro-fuzzy systems are used to create an ensemble of classifiers. After obtaining the ensemble by bagging, every neuro-fuzzy system has its t-norm parameters fine-tuned. Thanks to this the accuracy is improved and the number of parameters can be reduced. The proposed method is tested on a well known benchmark.
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页码：74 / +
页数：2
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