Geometrical meaning of domain of attraction and optimum robustness in noniterative neural networks
Chia-Lun John Hu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
This paper describes the basic N-dimension geometrical meaning of the noniterative neural network and the geometrical derivation of one of the most important properties of this neural network: The optimum robustness in the recognition of the untrained patterns. Based on this concept of optimum robustness, a novel automatic feature extraction system is derived. The predicted optimum robustness and the ultra-fast learning speed of this novel system are then verified experimentally. This paper concentrates at the geometrical derivations of this novel neural system design.