FUZZY NEURAL NETWORKS WITH REFERENCE NEURONS AS PATTERN CLASSIFIERS

被引:50
作者
PEDRYCZ, W
机构
[1] Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg
来源
IEEE TRANSACTIONS ON NEURAL NETWORKS | 1992年 / 3卷 / 05期
关键词
FUZZY SET LOGICAL CONNECTIVES; EQUALITY (REFERENCE) NEURON; HETEROGENEOUS NEURAL NETWORK; PATTERN CLASSIFIER; S-NORMS; T-NORMS;
D O I
10.1109/72.159065
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We will introduce a heterogeneous neural network consisting of logic neurons and realizing mappings in [0,1] hypercubes. The two kinds of neurons studied here are utilized to perform matching function (equality or reference neurons) and aggregation operations (aggregation neurons). All computations are driven by logic operations widely used in fuzzy set theory. The network is heterogeneous in its nature and includes two types of neurons organized into a structure detecting individual regions of patterns (using reference neurons) and combining them to yield a final classification decision.
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页码:770 / 775
页数:6
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