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Proceedings Paper

Geometrical meaning of domain of attraction and optimum robustness in noniterative neural networks
Author(s): Chia-Lun John Hu
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Paper 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.

Paper Details

Date Published: 28 March 2005
PDF: 3 pages
Proc. SPIE 5816, Optical Pattern Recognition XVI, (28 March 2005); doi: 10.1117/12.602947
Show Author Affiliations
Chia-Lun John Hu, Southern Illinois Univ./Carbondale (United States)


Published in SPIE Proceedings Vol. 5816:
Optical Pattern Recognition XVI
David P. Casasent; Tien-Hsin Chao, Editor(s)

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