The Modified Fuzzy Art and a Two-Stage Clustering Approach to Cell Design
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.
Subject
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
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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