Tracking, code recognition, and memory management with high-order neural networks

Clark D. Jeffries

doi:10.1117/12.205207

6 April 1995 Tracking, code recognition, and memory management with high-order neural networks

Clark D. Jeffries

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205207
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Clark D. Jeffries "Tracking, code recognition, and memory management with high-order neural networks", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205207

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