NEUROCOMPUTATIONAL APPROACH TO SOLVE A CONVEXLY COMBINED FUZZY RELATIONAL EQUATION WITH GENERALIZED CONNECTIVES

被引：3

作者：

CHUN, MG ^{[1
]}

BIEN, ZN ^{[1
]}

机构：

[1] KOREA ADV INST SCI & TECHNOL,DEPT ELECT ENGN,373-1 KUSONG DONG,TAEJON 305701,SOUTH KOREA

来源：

FUZZY SETS AND SYSTEMS | 1993年 / 57卷 / 03期

关键词：

FUZZY RELATIONAL EQUATIONS; NEURAL NETWORKS; IDENTIFICATION OF FUZZY SYSTEMS;

D O I：

10.1016/0165-0114(93)90027-F

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, we present a method to solve a convexly combined fuzzy relational equation with generalized connectives. For this, we propose a neural network whose structure represents the fuzzy relational equation. Then we derive a learning algorithm by using the concept of back-propagation learning. Since the proposed method can be used for a general form of fuzzy relational equations, such fuzzy max-min or min-max relational equations can be treated as its special cases.

引用

页码：321 / 333

页数：13

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