A study of parallel neural networks

Parallel Self-Organizing Map (Parallel-SOM) is proposed to modify Self-Organizing Map for parallel computing environments. In this model, the conventional repeated learning procedure is modified to learn just once. The once learning manner is more similar to human learning and memorizing activities. During training, every connection between neurons of input and output layers is considered as an independent processor. In this way, all elements of every matrix are calculated simultaneously. This synchronization feature improves the weight updating sequence significantly. In this paper, Parallel-SOM is implemented in a conventional computing environment (one processor), without the once learning and parallel weight updating features to show the correction of the algorithm. As an application, Parallel-SOM is used for the classification of the meteorological radar images.