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Proceedings Paper

System Realization Using Associative Memory Building Blocks
Author(s): Mark Carlotto; David Izraelevitz
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Paper Abstract

A data-based model of associative memory is described which uses statistical inference techniques to estimate an output response from a set of inputs and a database of previously stored patterns. The model is easily scaled in terms of the number of patterns that can be stored in the database as well as the number of fields in a pattern. Other features include the ability to change the input and output fields, to adjust the amount of generalization performed by the associative memory, and to control the size of the database by pruning redundant or conflicting patterns. Applications of associative memories to a wide variety of problems are illustrated to motivate their use as general system building blocks. Implementations in hardware and software are discussed.

Paper Details

Date Published: 1 March 1990
PDF: 11 pages
Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969758
Show Author Affiliations
Mark Carlotto, The Analytic Sciences Corporation (United States)
David Izraelevitz, The Analytic Sciences Corporation (United States)

Published in SPIE Proceedings Vol. 1192:
Intelligent Robots and Computer Vision VIII: Algorithms and Techniques
David P. Casasent, Editor(s)

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