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Proceedings Paper

Lattice associative memories that are robust in the presence of noise
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Paper Abstract

This paper presents a novel two-layer feedforward neural network that acts as an associative memory for pattern recall. The neurons of this network have dendritic structures and the computations performed by the network are based on lattice algebra. Use of lattice computation avoids multiplicative processes and, thus, provides for fast computation. The synaptic weights of the axonal fibers are preset, making lengthy training unnecessary. The proposed model exhibits perfect recall for perfect input vectors and is extremely robust in the presence of noisy or corrupted input.

Paper Details

Date Published: 12 September 2005
PDF: 6 pages
Proc. SPIE 5916, Mathematical Methods in Pattern and Image Analysis, 59160Q (12 September 2005); doi: 10.1117/12.622589
Show Author Affiliations
Gerhard X. Ritter, Univ. of Florida (United States)
Gonzalo Urcid-Serrano, INAOE (Mexico)
Mark S. Schmalz, Univ. of Florida (United States)

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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