Pattern classification using fuzzy sets and neural nets: a case-based approach

被引：0
|
作者：
De, RK [1 ]
Pal, SK [1 ]
机构：
[1] Indian Stat Inst, Machine Intelligence Unit, Calcutta 700035, W Bengal, India
来源：
ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS | 2000年 / 8卷 / 02期
关键词：
pattern classification; fuzzy sets; neural nets;
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：
The present article describes a case-based pattern classification method in connectionist framework using fuzzy set theory. Some labeled samples from each class are selected automatically as cases based on the concept of fuzzy similarity, and are represented as hidden nodes. The number of nodes is determined by the extent of fuzzy regions around cases. These are determined adaptively through growing and pruning under supervised training of the network. The effectiveness of the system, along with comparisons, has been demonstrated on various synthetic and real life pattern recognition problems for different extent of fuzzy regions.
引用
收藏
页码：103 / 108
页数：6
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