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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References
I. Bloch (1996), “Information Combination Operators for Data Fusion: A Comparative Review with Classification”. IEEE Transaction On SMC, part A,Vol. 26, N°1, pp 5267, 1996.
Y. Cheng and R. L. Kashyap (1992), “Recursive Fusion Operators: Desirable properties and illustrations”. Data Fusion in Robotics and Machine Intelligence“, Ed. M. A. Abidi and R. C. Gonzalez, Academic Press inc, 1992.
Brown M, Harris C.J, “Neural Networks for Modeling and Control” in Advances in Intelligent Control, Taylor Francis Lts, pp. 17–57, ISBN 0–7484–0066–4.
A. Sachenko, V. Kochan, V. Turchenko, V. Golovko, J. Savitsky, A. Dunets, T. Laopoulos (2000), “Sensor errors prediction using neural networks”, Proceedings of the IJCNN’2000 Conf., Jul 24-Jul 27 2000, 2000, Como, Italy, p 441–446.
K. Madani, A. Chebira, R. Reynaud, T. Maurin and K. Bouchefra (1998), “Hybrid neural-based decision level fusion architecture: application to road traffic collision avoidance”, Optical Engineering 37 (2), 1998.
Nageswara S. V. Rao (1997), “Fusion Rule Estimation Using Vector Space Methods” Proceedings of SPIE, Sensor Fusion: Architectures, Algorithms, and Applications, volume 3067, pp 130–135, 24–24 April 1997, Orlando, Florida.
A. Sachenko (1999), “IMCS development on the basis of distributed sensor networks”, IEEE AFRICON Conference, vl, 1999 IEEE, Piscataway, NJ, USA, p 345–350.
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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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