Global stability analysis of a nonlinear principal component analysis neural network
Anke Meyer‐Baese · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
The self-organization of a nonlinear, single-layer neural network is mathematically analyzed, in which a regular Hebbian rule and an anti-Hebbian rule are used for the adaptation of the connection weights between the constituent units. It is shown that the equilibrium points of this system are global asymptotically stable. Following some restrictive assumptions a nonlinear principal component analyzer can be constructed.