Boosting ensemble of relational neuro-fuzzy systems

被引：0
|
作者：
Scherer, Rafal
机构：
[1] Czestochowa Tech Univ, Dept Comp Engn, PL-42200 Czestochowa, Poland
[2] WSHE Univ Lodz, Dept Artificial Intelligence, Lodz, Poland
来源：
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING - ICAISC 2006, PROCEEDINGS | 2006年 / 4029卷
关键词：
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 the paper a boosting ensemble of neuro-fuzzy relational systems is created. Rules in relational fuzzy systems are more flexible than rules in linguistic fuzzy systems because of the additional weights in rule consequents. The weights come from an additional binary relation. Thanks to this, input and output fuzzy sets are related to each other with a certain degree. The size of the relations is determined by the number of input fuzzy sets and the number of output fuzzy sets. Simulations performed on popular benchmarks show that the proposed ensemble outperforms other learning systems.
引用
收藏
页码：306 / 313
页数：8
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