Optimising the clustering performance of a self-organising logic neural network with topology-preserving capabilities

In this article, a self-organising logic neural network is studied. This network successfully clusters input patterns into classes characterised by a high similarity, while assigning these classes to the network nodes so that relationships existing in the pattern space are replicated on the network structure. The network performance is optimised by (i) introducing a mechanism which ensures the efficient use of the network nodes for storage of pattern classes and by (ii) determining the training strategy which results in optimal topology-preservation characteristics.