Finite-precision discrete-time neural network data association

Oluseyi Olurotimi; Clayton V. Stewart; Roger Novack

doi:10.1117/12.140013

16 September 1992 Finite-precision discrete-time neural network data association

Oluseyi Olurotimi, Clayton V. Stewart, Roger Novack

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.140013
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Oluseyi Olurotimi, Clayton V. Stewart, and Roger Novack "Finite-precision discrete-time neural network data association", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.140013

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