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Proceedings Paper

Pattern recognition and classification using weightless neural networks (WNN) and Steinbuch Lernmatrix
Author(s): Amadeo Jose Argüelles C.; Juan Luis Díaz de León S.; Cornelio Yáñez M.; Oscar Camacho N.
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Paper Abstract

This proposal presents a novel use of Weightless Neural Networks (WNN) and Steinbuch Lernmatrix for pattern recognition and classification. High speed of learning, easy of implementation and flexibility given by WNN, combined with the learning capacity, recovery efficiency, noise immunity and fast processing shown by Steinbuch Lernmatrix are key factors considered on the pattern recognition exposed by the suggested model. For experimental purposes, the fundamental pattern sets are built and provided to the model under the learning phase. The additive, subtractive and mixed noises are applied to fundamental patterns to check out the response of the model during the recovery phase. Field Programmable Gate arrays are used in the implementation of such model, since it allows custom user-defined models to be embedded in a reconfigurable hardware platform, and provides block memories and dedicated multipliers suitable for the model.

Paper Details

Date Published: 12 September 2005
PDF: 8 pages
Proc. SPIE 5916, Mathematical Methods in Pattern and Image Analysis, 59160P (12 September 2005); doi: 10.1117/12.621783
Show Author Affiliations
Amadeo Jose Argüelles C., Instituto Politecnico Nacional (Mexico)
Juan Luis Díaz de León S., Instituto Politecnico Nacional (Mexico)
Cornelio Yáñez M., Instituto Politecnico Nacional (Mexico)
Oscar Camacho N., Instituto Politecnico Nacional (Mexico)

Published in SPIE Proceedings Vol. 5916:
Mathematical Methods in Pattern and Image Analysis
Jaakko T. Astola; Ioan Tabus; Junior Barrera, Editor(s)

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