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Proceedings Paper

Finite-precision discrete-time neural network data association
Author(s): Oluseyi Olurotimi; Clayton V. Stewart; Roger Novack
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Paper Abstract

This paper describes a discrete-time analog neural network solution to the data association, or data correlation problem. This work, which is an extension of previous investigations, was originally motivated by the earlier results of Sengupta and Iltis (1989). In this paper, we exploit the fact that the associated optimization problem is loosely described, and map the data association problem onto an analog discrete-time neural network connected in an on-center, off-surround configuration. This reduces the number of parameters required in the system design, thereby also reducing the system sensitivity to parameter variations, and leading to greater robustness. Results are presented for simulations performed on a typical workstation. Simulations were also performed with reduced precision numbers. The performance in both cases were not identical, and parameter adjustments in a specific direction are needed in the finite-precision case for acceptable performance.

Paper Details

Date Published: 16 September 1992
PDF: 12 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140013
Show Author Affiliations
Oluseyi Olurotimi, George Mason Univ. (United States)
Clayton V. Stewart, George Mason Univ. (United States)
Roger Novack, George Mason Univ. (United States)


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

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