Convergence properties of self-organizing neural networks
Roberto Horowitz, Luis Alvarez · 2005
In this paper we analyze the convergence properties of a class of self-organizing neural networks, introduced and popularized by Kohonen, using the ODE approach. It is shown that Kohonen's learning law converges to the best locally affine feature map. A new integrally distributed self-organizing learning law is presented which converges to the equiprobable feature map for inputs which have arbitrary random probability distribution functions.