Machine-part family formation utilizing an art1 neural network implemented on a parallel neuro-computer

The ART1 neural network algorithm has been implemented in the past for the classifying and grouping of similar vectors from a machine-part matrix. Recently, a new ART1 paradigm which involves reordering of the input vectors with a modified procedure for storing a groupʹs representation vectors has proven successful in both speed and functionally compared to previous techniques. This new paradigm is now adapted and implemented on a neuro-computer utilising 256 processors, allowing the neural network to take advantage of its inherent parallelism. Tremendous improvements in the speed of the machine-part matrix optimization result from the parallel implementation. Comparisons with the previous serial algorithm are made and suggestions for possible parallel implementation within a manufacturing environment are discussed.