An unsupervised neural network approach for machine-part cell design

Abstract

We develop an unsupervised learning clustering neural network method for designing machine-part cells in cellular manufacturing. Our approach is based on the competitive learning algorithm. We use the generalized Euclidean distance as similarity measurement, and add a momentum term in the weight vector updating equations. The cluster structure can be adjusted by changing the coefficients in the generalized Euclidean distance. We also develop a neural network clustering system which can be used to cluster a 0-1 matrix into diagonal blocks. The developed neural network clustering system is independent of the initial matrix and gives clear final clustering results which specify the machines and parts in each group. We use the developed neural network clustering system to solve an example, in which the machine-part incidence matrix is to be clustered into diagonal block structure. The computational results are compared with those from the rank order clustering and directive clustering analysis methods. >