A METHOD FOR FUZZY RULES EXTRACTION DIRECTLY FROM NUMERICAL DATA AND ITS APPLICATION TO PATTERN-CLASSIFICATION

被引：200

作者：

ABE, S ^{[1
]}

LAN, MS ^{[1
]}

机构：

[1] ASAHI DIAMOND IND CO LTD,CHIYODA KU,TOKYO 102,JAPAN

来源：

IEEE TRANSACTIONS ON FUZZY SYSTEMS | 1995年 / 3卷 / 01期

关键词：

Inference engines - Knowledge acquisition - Neural networks - Pattern recognition - Recursive functions;

D O I：

10.1109/91.366565

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we discuss a new method for extracting fuzzy rules directly from numerical input-output data for pattern classification. Fuzzy rules with variable fuzzy regions are defined by activation hyperboxes which show the existence region of data for a class and inhibition hyperboxes which inhibit the existence of data for that class. These rules are extracted from numerical data by recursively resolving overlaps between two classes. Then, optimal input variables for the rules are determined using the number of extracted rules as a criterion. The method is compared with neural networks using the Fisher iris data and a license plate recognition system for various examples.

引用

页码：18 / 28

页数：11

共 11 条

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