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Proceedings Paper

Tracking, code recognition, and memory management with high-order neural networks
Author(s): Clark D. Jeffries
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Paper Abstract

A variety of tracking, recognition, and routing problems can be expressed as choices of rectangular 0,1 matrices. For some such problems of practical significance, neural network systems can give higher quality solutions than any other method. For example, in a tracker, how well predicted position i fits observed position j can generate through a metric function (possibly a backpropagation neural net), the initial ij entry in a large rectangular matrix. Using that matrix as an initial state, an associator dynamical system neural network can then choose a 0,1 matrix of the same dimensions with at most one 1 in each row and at most one 1 in each column. The association optimizes some overall goodness of fit criteria, not in general the same as pairwise goodness of fit. Related high order neural networks can be used to solve other finite choice problems including error-correction of binary codes and computer memory management. This paper is an overview of mathematical foundations of such dynamical systems.

Paper Details

Date Published: 6 April 1995
PDF: 10 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205207
Show Author Affiliations
Clark D. Jeffries, Clemson Univ. (United States)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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