Geometrical meaning of domain of attraction and optimum robustness in noniterative neural networks

Chia-Lun John Hu

doi:10.1117/12.602947

28 March 2005 Geometrical meaning of domain of attraction and optimum robustness in noniterative neural networks

Chia-Lun John Hu

Author Affiliations +

Proceedings Volume 5816, Optical Pattern Recognition XVI; (2005) https://doi.org/10.1117/12.602947
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Chia-Lun John Hu "Geometrical meaning of domain of attraction and optimum robustness in noniterative neural networks", Proc. SPIE 5816, Optical Pattern Recognition XVI, (28 March 2005); https://doi.org/10.1117/12.602947

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