Self-Organizing Map Convergence

The Time Adaptive Self Organizing Map for Distribution Estimation

The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...

Convergence Properties of Self-organizing Neural Networks

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 a ne feature map. A new integrally distributed self-organizing learning law which converges to the equiprobable feature map for inputs which have arbitrary random pr...

Local Adaptive Receptive Field Self-organizing Map for Image Segmentation

A new self-organizing map with variable topology is introduced for image segmentation. The proposed network, called Local Adaptive Receptive Field Self-organizing Map (LARFSOM-RBF), is a two-stage network capable of both color and border segment images. The color segmentation stage is responsibility of LARFSOM which is characterized by adaptive number of nodes, fast convergence and variable top...

Diffusion approximation of frequency sensitive competitive learning

The focus of this paper is a convergence study of the frequency sensitive competitive learning (FSCL) algorithm. We approximate the final phase of FSCL learning by a diffusion process described by the Fokker-Plank equation. Sufficient and necessary conditions are presented for the convergence of the diffusion process to a local equilibrium. The analysis parallels that by Ritter-Schulten (1988) ...

Understanding Eurasian Convergence: Application Of Kohonen Self-Organizing Maps