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Proceedings Paper

Neural networks and genetic algorithms for combinatorial optimization of sensor data fusion
Author(s): Angel L. DeCegama; Jeffrey E. Smith
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Paper Abstract

The sensor data fusion problem can be formulated as a combinatorial optimization problem. Simulated annealing is a technique based on an analogy with the physical process of annealing which can find solutions to such problems arbitrarily close to an optimum. However, the computational effort involved can be prohibitive especially to obtain high quality solutions of large problems. Parallel processing offers the capability to provide the required computational power for real time performance in sensor data fusion applications by taking advantage of the massive parallelism and distributed representations of neural networks. Several types of neural networks, e.g., Gaussian, Boltzmann, and Cauchy machines, have been proposed to implement the technique of simulated annealing in parallel according to different cooling schedules but such neural networks have not previously been analyzed in terms of their capabilities for the specific problem of sensor data fusion. This paper presents the results of research conducted in order to evaluate the neural network approach to the combinatorial optimization problem intrinsic in real time sensor data fusion. A comparison with other advanced technique, i.e., genetic algorithms, is also being investigated.

Paper Details

Date Published: 9 July 1992
PDF: 8 pages
Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); doi: 10.1117/12.138215
Show Author Affiliations
Angel L. DeCegama, Lockheed Sanders, Inc. (United States)
Jeffrey E. Smith, Lockheed Sanders, Inc. (United States)


Published in SPIE Proceedings Vol. 1699:
Signal Processing, Sensor Fusion, and Target Recognition
Vibeke Libby; Ivan Kadar, Editor(s)

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