USING INDUCTIVE LEARNING TO DETERMINE FUZZY RULES FOR DYNAMIC-SYSTEMS

被引：5

作者：

SRINIVASAN, A ^{[1
]}

BATUR, C ^{[1
]}

CHAN, CC ^{[1
]}

机构：

[1] UNIV AKRON,AKRON,OH 44325

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 1993年 / 6卷 / 03期

关键词：

DECISION TREES; DYNAMIC SYSTEMS; FUZZY MODELING; INDUCTIVE LEARNING SYSTEMS; PRODUCTION RULES; REFERENCE FUZZY SETS;

D O I：

10.1016/0952-1976(93)90068-9

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper describes a fuzzy model-building methodology. Process input-output data are quantized by fuzzy reference membership functions. Membership values are determined for each data point and only the membership function that generates the maximum membership value is used. The name of membership functions corresponding to maximum values are fed into an inductive learning algorithm and the fuzzy rules are determined. The methodology is applied to fuzzy modeling of a dynamic system.

引用

页码：257 / 264

页数：8

共 18 条

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[2]

[Anonymous], 1981, PATTERN RECOGN

[3]

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[4]

Breiman L, 2017, CLASSIFICATION REGRE, P368, DOI 10.1201/9781315139470