Dynamic topology representing networks

We propose an algorithm called dynamic topology representing networks (DTRN) for learning both topology and clustering information from input data. In contrast to other models with adaptive architecture of this kind, the DTRN algorithm adaptively grows the number of output nodes by applying a vigilance test. The clustering procedure is based on a winner-take-quota learning strategy in conjunction with an annealing process in order to minimize the associated mean square error. A competitive Hebbian rule is applied to learn the global topology information concurrently with the clustering process. The topology information learned is also utilized for dynamically deleting the nodes and for the annealing process. The specific properties of this new algorithm are illustrated by some analyses and simulation examples