Determination of fuzzy decision fusion system parameters by genetic algorithms

Anna Loskiewicz-Buczak; Robert E. Uhrig

doi:10.1117/12.169960

2 March 1994 Determination of fuzzy decision fusion system parameters by genetic algorithms

Anna Loskiewicz-Buczak, Robert E. Uhrig

Proceedings Volume 2243, Applications of Artificial Neural Networks V; (1994) https://doi.org/10.1117/12.169960
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

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.

Citation Download Citation

Anna Loskiewicz-Buczak and Robert E. Uhrig "Determination of fuzzy decision fusion system parameters by genetic algorithms", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.169960

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