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.

引用

页码：770 / 775

页数：6

共 7 条

[1]

Duda R. O., 1973, PATTERN CLASSIFICATI, V3

[2]

Kohonen T., 1986, TKKFA601 HELS U TECH

[3]

NASRABADI NM, 1988, P IEEE INT C NEURAL, V1, P101

[4]

Pao Y.-H., 1991, IEEE Transactions on Knowledge and Data Engineering, V3, P185, DOI 10.1109/69.87998