Publication:
The Modified Fuzzy Art and a Two-Stage Clustering Approach to Cell Design

Date
2007-12-01
Authors
Özdemir, Rifat Gürcan
Gençyılmaz, Güneş
Aktin, Ayşe Tülin
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ELSEVIER SCIENCE INC, 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA
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Abstract

This study presents a new pattern recognition neural network for clustering problems, and illustrates its use for machine cell design in group technology. The proposed algorithm involves modifications of the learning procedure and resonance test of the Fuzzy ART neural network. These modifications enable the neural network to process integer values rather than binary valued inputs or the values in the interval [0, 1], and improve the clustering performance of the neural network. A two-stage clustering approach is also developed in order to obtain an informative and intelligent decision for the problem of designing a machine cell. At the first stage, we identify the part families with very similar parts (i.e., high similarity exists in their processing requirements), and the resultant part families are input to the second stage, which forms the groups of machines. Experimental studies show that the proposed approach leads to better results in comparison with those produced by the Fuzzy ART and other similar neural network classifiers. (C) 2007 Elsevier Inc. All rights reserved.

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Keywords
Intelligent Manufacturing , Artificial Neural Network , Group Tchnology , Clustering , Machine Cell Formation , Neural-Network Approach , Self-Organizing Map , Group Technology , Machine , Algorithm , Performance , Extension , system , Akıllı İmalat , Yapay Sinir Ağları , Grup Teknolojisi , Kümeleme , Makine Hücre Oluşumu , Sinir-Ağı Yaklaşımı , Kendi Kendini Düzenleyen Haritası , Makine , Algoritma , Performans , Uzatma , Sistem
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