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Proceedings Paper

Determination of fuzzy decision fusion system parameters by genetic algorithms
Author(s): Anna Loskiewicz-Buczak; Robert E. Uhrig
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Paper Abstract

This paper describes a decision fusion method based on fuzzy logic and genetic algorithms. For the fusion process the generalized mean aggregation connective is used. The optimal parameters of the generalized mean are found by a genetic algorithm both with elitist and nonelitist strategy. The results of both strategies are compared. The decision fusion method proposed is tested on a vibration monitoring problem. The decisions from multiple sensors to be fused are obtained by neural networks. First vibration spectra are compressed by recirculation networks. Next classification of compressed signatures is performed for each sensor separately by backpropagation networks. The output of backpropagation networks is the input to the fuzzy fusion center performing the generalized mean operation.

Paper Details

Date Published: 2 March 1994
PDF: 12 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169960
Show Author Affiliations
Anna Loskiewicz-Buczak, Univ. of Tennessee/Knoxville (United States)
Robert E. Uhrig, Univ. of Tennessee/Knoxville and Oak Ridge National Lab. (United States)

Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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