Selecting the toroidal self-organizing feature maps (TSOFM) best organized to object recognition

Self-organizing feature maps (SOFM) are an important tool to visualize high-dimensional data as a two-dimensional image. One of the possible applications of this network is in image recognition. However, this architecture presents some problems mainly due to border effects. In this paper a new organization of the feature maps termed toroidal self-organizing feature maps (TSOFM) is presented. Its main advantage consists in the elimination of the border effects and, consequently, an increase in the recognition rate. Another important aspect presented in this paper is the measurement of how well networks are organized during the training phase. This proposal has been experimented with using a real data set