Improved rate of convergence in Kohonen neural network

The neighborhood interaction function selection in the Kohonen self-organizing feature map neural network is analyzed for improving the rate of convergence. The definition of the neighborhood interaction function is motivated by anatomical evidence as opposed to what is currently used, which is a uniform neighborhood interaction set. By selecting a neighborhood interaction function with a neighborhood amplitude of interaction which is decreasing in the spatial domain the topological order is always enforced and the rate of self-organization to final equilibrium state is improved. A simulation is carried out to show the convergence rate improvement achieved using a neighborhood interaction function vs. using a neighborhood interaction set. An error measure functional is further defined to compare the two approaches quantitatively