New model of self-organizing neural networks and its application in data projection

In this paper a new model of self-organizing neural networks is proposed, A n algorithm called "double self-organizing feature map" (DSOM) algorithm is developed to train the novel model, By the DSOM algorithm the network will adaptively adjust its network structure during the learning phase so as to make neurons responding to similar stimulus have similar weight vectors an d spatially move nearer to each other at the same time. The final network s tructure allows us to visualize high-dimensional data as a two-dimensional scatter plot. The resulting representations allow a straightforward analysi s of the inherent structure of clusters within the input data. One high-dim ensional data set is used to test the effectiveness of the proposed neural networks.