A Comparative Study of Different Neighborhood Topologies in WTM Kohonen Self-Organizing Maps
In this paper we present a software model of the Winner Takes Most (WTM) Kohonen neural network (KNN) with different types of the neighborhood grid. The proposed network model allows for analysis of the convergence properties such as the quantization error and the convergence time for different grids, which is essential looking from the hardware implementation point of view of such networks. Particular grids differ in complexity, which in hardware implementation has a direct influence on power dissipation as well as on chip area and the final production cost. The presented results show that even the simplest rectangular grid with four neighbors allows for good convergence properties for different training data files.