Learning maximal structure rules in fuzzy logic for knowledge acquisition in expert systems

被引：56

作者：

Castro, JL ^{[1
]}

Castro-Schez, JJ ^{[1
]}

Zurita, JM ^{[1
]}

机构：

[1] Univ Granada, ETSI Informat, Dept Ciencias Computac & IA, E-18071 Granada, Spain

来源：

FUZZY SETS AND SYSTEMS | 1999年 / 101卷 / 03期

关键词：

inductive learning; fuzzy logic; expert system;

D O I：

10.1016/S0165-0114(97)00105-X

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The aim of this article is to present a new approach to machine learning (precisely in classification problems) in which the use of fuzzy logic has been taken into account. We intend to show that fuzzy logic introduces new elements in the identification process, mainly due to the facility to manage imprecise information. An inductive algorithm generating a set of fuzzy rules identifying the system will be achieved. The maximal structure of a fuzzy rule will be found using this algorithm. (C) 1999 Elsevier Science B.V. All rights reserved.

引用

页码：331 / 342

页数：12

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