An incremental unsupervised learning scheme for function approximation

A new algorithm for general robust function approximation by an artificial neural network is presented. The basis for this work is Fritzke's supervised growing cell structures approach (1993) which combines supervised and unsupervised learning. It is extended by the capability of resampling the function under examination automatically, and by the definition of a new error measure which enables an accurate approximation of arbitrary goal functions.