NEURAL NETWORKS AND THE PART FAMILY MACHINE GROUP FORMATION PROBLEM IN CELLULAR MANUFACTURING - A FRAMEWORK USING FUZZY ART

被引：30
作者：
BURKE, L
KAMAL, S
机构：
[1] Lehigh University, Bethlehem, PA
来源：
JOURNAL OF MANUFACTURING SYSTEMS | 1995年 / 14卷 / 03期
基金：
美国国家科学基金会;
关键词：
NEURAL NETWORKS; CELLULAR MANUFACTURING; PART FAMILY FORMATION; GROUP TECHNOLOGY;
D O I：
10.1016/0278-6125(95)98883-8
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
We apply the fuzzy adaptive resonance theory (ART) neural network to the part family/machine group formation problem in cellular manufacturing. Previous neural network applications have demonstrated the potential role of competitive learning and ART networks in part family/machine cell formation, but they have a number of shortcomings. Fuzzy ART, based on a similarity measure from fuzzy set theory, shows great promise over other approaches. We present results for fuzzy ART applied to several test problems of part family formation and give an extension for systematically generating alternative solutions in the problem domain.
引用
收藏
页码：148 / 159
页数：12
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