Relation organization of som initial map by node exchange with fixed neighbor areas

The Self Organizing Map (SOM) involves neural networks, that learns the features of input data thorough unsupervised, competitive neighborhood learning. In the SOM learning algorithm, connection weights in a SOM feature map are initialized at random values, which also sets nodes at random locations in the feature map independent of input data space. The move distance of output nodes increases, slowing learning convergence. As precedence research, we proposed the method to improve this problem, initial node exchange by using a part of feature map. In this paper, we propose new exchange method, node exchange with fixed neighbor area. The method uses fixed position of winner node and fixed initial size of neighbor area that sets to cover whole feature map. We investigate how average move distance of all nodes and average deviation of move distance would change with the differences by type of fixed neighbor area in node exchange process. As a result of experiments, sufficient effect is acquired by fixed 1 neighbor area in the viewpoint of both average move distance of all nodes and average deviation of move distance.