Abstract
In this paper, we present a Functional Link Network (FLN) based neural technique for sensors issued data fusion. Thanks to a pruning algorithm, we build dynamically the internal layer of the FLN, constructing an optimal architecture of the FLN neural network defining an optimal fusion policy. As the neural FLN minimize the mean square error (MSE) during the learning step, an optimal fusion policy is reached in the sense of the MSE. Experimental results related to performances enhancement of metric sensors have been reported validating our approach.
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© 2003 Springer-Verlag Berlin Heidelberg
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Chebira, A., Madani, K. (2003). A Neural Network Based Approach For Sensors Issued Data Fusion. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_20
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DOI: https://doi.org/10.1007/978-3-7908-1902-1_20
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-0005-0
Online ISBN: 978-3-7908-1902-1
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