Function approximation with hyperplan-based self-organising maps

This paper presents an optimized variant of HYPSOM, a network of self-organised hyperplans, meant for the approximation of multivariable functions. This network, initially equipped with a fixed structure, is now presented with a growing structure. This study allowed the validation of a learning algorithm, based on the addition and elimination of neurons, thus inducing the adaptation of the network structure to the arbitrary complexity of a function