A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Fuzzy logic and neural network handbook
Fuzzy logic and neural network handbook
Expanding self-organizing map for data visualization and cluster analysis
Information Sciences: an International Journal - Special issue: Soft computing data mining
An effective learning of neural network by using RFBP learning algorithm
Information Sciences—Informatics and Computer Science: An International Journal
Supervised learning on a fuzzy Petri net
Information Sciences—Informatics and Computer Science: An International Journal
A comparison of classification accuracy of four genetic programming-evolved intelligent structures
Information Sciences: an International Journal
Iterative Bayesian fuzzy clustering toward flexible icon-based assistive software for the disabled
Information Sciences: an International Journal
On path dependent loss and switch crosstalk reduction in optical networks
Information Sciences: an International Journal
A hybrid neural network approach to cell formation in cellular manufacturing
International Journal of Intelligent Systems Technologies and Applications
Computers and Industrial Engineering
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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.