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Proceedings Paper

Optimal fusion operator selection: a neural-network-technique-based approach
Author(s): Abdennasser Chebira; Kurosh Madani
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Paper Abstract

In this paper, we present a neural network based method that allows the optimal selection of a data fusion policy. We build dynamically the internal layer of a functional link network (FLN), we add to the classical FLN, a pruning algorithm, that allows to find the optimal architecture of the FLN and to define an optimal fusion policy. In order to use the FLN as a universal fusion operator, the functional expansion performed by its internal layer includes fusion operators. As the FLN minimize the mean square error (MSE) during the learning step, an optimal fusion policy is reached in the sense of the MSE. Some academic simulations validate our approach.

Paper Details

Date Published: 25 March 1998
PDF: 10 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304835
Show Author Affiliations
Abdennasser Chebira, Univ. Paris XII (France)
Kurosh Madani, Univ. Paris XII (France)


Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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