Induction of fuzzy rules and membership functions from training examples

被引：232

作者：

Hong, TP ^{[1
]}

Lee, CY ^{[1
]}

机构：

[1] CHUNG HUA POLYTECH INST, INST ELECT ENGN, HSINCHU 30067, TAIWAN

来源：

FUZZY SETS AND SYSTEMS | 1996年 / 84卷 / 01期

基金：

美国国家科学基金会;

关键词：

expert systems; fuzzy clustering; fuzzy decision rules; fuzzy machine learning; knowledge acquisition; membership functions;

D O I：

10.1016/0165-0114(95)00305-3

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Most fuzzy controllers and fuzzy expert systems must predefine membership functions and fuzzy inference rules to map numeric data into linguistic variable terms and to make fuzzy reasoning work. In this paper, we propose a general learning method as a framework for automatically deriving membership functions and fuzzy if-then rules from a set of given training examples to rapidly build a prototype fuzzy expert system. Based on the membership functions and the fuzzy rules derived, a corresponding fuzzy inference procedure to process inputs is also developed.

引用

页码：33 / 47

页数：15

共 29 条

[1]

[Anonymous], 2 INT C FUZZ LOG NEU

[2]

[Anonymous], IEEE COMPUT

[3]

[Anonymous], MACHINE LEARNING ART

[4]

[Anonymous], FUZZY EXPERT SYSTEMS

[5]

[Anonymous], 1991, FUZZY SET THEORY ITS

[6]

BERENJI HR, 1992, INTRO FUZZY LOGIC AP, P45

[7]

Buchanan B.G., 1984, Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)

[8]

BURKHARDT DG, 1992, MAR P FUZZ IEEE 92 S, P179

[9] The use of multiple measurements in taxonomic problems [J].

Fisher, RA .

ANNALS OF EUGENICS, 1936, 7 :179-188

[10]

GRAHAM I, 1988, EXPERT SYSTEMS KNOWL, P117

← 1 2 3 →