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Proceedings Paper

Simplified ART1
Author(s): Peggy Israel; S. Yu; P. Ryan
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Paper Abstract

Simplified ART1 performs unsupervised classification of binary input patterns, forming prototypes of object classes by grouping similar objects together. The model is based on ART1, but uses a new similarity measure and activation function which eliminate the need for sequential search through previously learned prototypes. The new similarity measure minimizes recoding of inputs into different categories, making stabilization faster. Simplified ART1 requires only half the weights of ART1, which makes it easier to implement. In summary, we introduce modifications to ART1 which produce a faster, more efficient and simpler model. Results obtained in software simulations are used to compare the performance of the ART1 and Simplified ART1 models.

Paper Details

Date Published: 16 September 1992
PDF: 10 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140025
Show Author Affiliations
Peggy Israel, Univ. of Alabama in Huntsville (United States)
S. Yu, Univ. of Alabama in Huntsville (United States)
P. Ryan, Univ. of Alabama in Huntsville (United States)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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