Increasing the topological quality of Kohonen's self-organising map by using a hit term

The quality of the topology obtained at the end of the training period of Kohonen's Self Organizing Map (SOM) is highly dependent on the learning rate and neighborhood function that are chosen at the beginning. The conventional approaches to determine those parameters do not account for the data statistics and the topological characterization of the neurons. This paper proposes a new parameter, which depends on the hit ratio among the updated. neuron and the best matching neuron. It has been shown that by using this parameter with the conventional learning rate and neighborhood functions, much more adequate solution can be obtained since it deserves an information about data statistics during adaptation process.