A nonlinear extension of the Generalized Hebbian learning

摘要

In this letter, we introduce a nonlinear hierarchic PCA type neural network with a simple architecture. The learning algorithm is a kind of nonlinear extension of the well-known Sanger's Generalized Hebbian Algorithm (GHA). It is derived from a nonlinear optimization criterion. Experiments with sinusoidal data show that the neurons become sensitive to different sinusoids. Standard linear PCA algorithms don't have such a separation property.