On-line kernel clustering based on the general regression neural network and T. Kohonen’s self-organizing map

Abstract

The clustering system based on the evolving general regression neural network and self-organizing map of T.Kohonen, is proposed in the paper. The tuning of system is based on “lazy” learning and self-learning using the principle “Winner takes more” at the same time as neighborhood function the output signal of the hybrid network is used. The system’ implementation is characterized by numerical simplicity. The evolving neural network processes data in an online mode and doesn’t suffer from the curse of dimensionality.