Implementation of Parallel Self-Organizing Map to the classification of image

A study of 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/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, the detail sequence of Parallel-SOM is demonstrated through the classification of coin for deeply understanding the properties of the proposed model. in conventional computing environment (one processor), Parallel-SOM can be implemented without the once learning and parallel weight updating features. As an application, its implementation for the classification of the meteorological radar images is also shown.