A NEURAL-NETWORK-BASED FUZZY CLASSIFIER

被引：53

作者：

UEBELE, V ^{[1
]}

ABE, S ^{[1
]}

LAN, MS ^{[1
]}

机构：

[1] ROCKWELL INT CORP,CTR SCI,THOUSAND OAKS,CA 91358

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS | 1995年 / 25卷 / 02期

关键词：

D O I：

10.1109/21.364829

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a new technique for generating fuzzy rules for pattern classification is discussed. First, separation hyperplanes for classes are extracted from a trained neural network. Then, for each class, convex existence regions in the input space are approximated by shifting these hyperplanes in parallel using the training data set for the classes. Using fuzzy rules defined for each class, input data are directly classified without the use of the neural network. This method is applied to a number recognition system as well as to a blood cell classification system. Classifying performance is compared with that obtained with neural networks.

引用

页码：353 / 361

页数：9

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