GENERATING FUZZY MEMBERSHIP FUNCTIONS - A MONOTONIC NEURAL-NETWORK MODEL

被引：32

作者：

WANG, SH

机构：

[1] Faculty of Business, University of New Brunswick, Saint John

来源：

FUZZY SETS AND SYSTEMS | 1994年 / 61卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

关键词：

FUZZY SETS; ULTRAFUZZY; NEURAL NETWORKS; BACK PROPAGATION ALGORITHM; MONOTONICITY;

D O I：

10.1016/0165-0114(94)90286-0

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

A crucial issue in the practical applications of fuzzy sets is to find a fuzzy membership function. This paper suggests that neural networks which use the back propagation learning algorithm under monotonic function constraints can be used in generating fuzzy membership functions.

引用

页码：71 / 81

页数：11

共 25 条

[1]

[Anonymous], 1992, NEURAL NETWORKS FUZZ

[2] FUZZY SET REPRESENTATION OF NEURAL NETWORK CLASSIFICATION BOUNDARIES [J].