PERFORMANCE OF FUZZY ART NEURAL-NETWORK FOR GROUP TECHNOLOGY CELL-FORMATION

This study investigates the performance of Fuzzy ART neural network fo r grouping parts and machines, as part of the design of cellular manuf acturing systems. Fuzzy ART is compared with ART1 neural network and a modification to ART1, along with direct clustering analysis (DCA) and rank order clustering (ROC2) algorithms. A series of replicated clust ering experiments were performed, and the efficiency and consistency w ith which clusters were identified were examined, using large data set s of differing sizes and degrees of imperfection. The performance meas ures included the recovery ratio of bond energy and execution times. I t is shown that Fuzzy ART neural network results in better and more co nsistent identification of block diagonal structures than ART1, a rece nt modification to ART1, as well as DCA and ROC2. The execution times were found to be more than those of ART1 and modified ART1, but they w ere still superior to traditional algorithms for large data sets.