Abstract
In this paper, a tentative of prediction of Instrument Langmuir (ISL) installed in the DEMETER satellite is implanted. Prediction is based on the Multilayer Perceptron (MLP) neural network model. The MLP machine is composed of three layers, an input layer with four neurons, a hidden layer with ten neurons and an output layer with the same number of units like the input layer. Parameters to be predicted are electrons and ions density, electrons temperatures and plasma potential. Application to the data of orbit 27447-1 recorded two days before the Laquila earthquake of 06 April 2009 clearly shows the power of the artificial neural network in the prediction of ionospheric perturbations and Plasma analysis.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Peterson, C.: Neural Networks in High Energy Physics. In: Plenary talk presented at the "Computing in High Energy Physics", Annecy, France, September 21-25, 1991 (1992)
Lynch, M., Patel, H., Abrahamse, A., Rupa Rajendran, A., Medsker, L.: Neural network applications in physics. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2001, vol. 3, pp. 2054–2058 (2001), doi:10.1109/IJCNN.2001.938482
Leberton, J.-P.: On the issue of surface contamination of a Langmuir Probe sesor: Demter IS results. Geophysical Research Abstrcats 14, EGU2012-13806
Ouadfeul, S.-A., Aliouane, L.: Lithofacies prediction from well log data using a multilayer perceptron (MLP) and Kohonen’s self-organizing map (SOM) – a case study from the Algerian Sahara. Pattern Recogn. Phys. 1, 59–62 (2013), doi:10.5194/prp-1-59-2013
Ouadfeul, S.-A., Aliouane, L.: Lithofacies Classification Using the Multilayer Perceptron and the Self-organizing Neural Networks. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part V. LNCS, vol. 7667, pp. 737–744. Springer, Heidelberg (2012)
Teng, W., Xiang-Dong, G., Wei, L.: Characterisation of the plasma density with two artificial neural network models. Chinese Phys. B 19, 070505 (2010), doi:10.1088/1674-1056/19/7/070505
Wei, L., Jun-Fang, C., Teng, W.: Prediction of the plasma distribution using an artificial neural network. Chinese Phys. B 18 2441 (2009), doi:10.1088/1674-1056/18/6/053
Zhang, X., Qian, J., Ouyang, X., Shen, X., Cai, J., Zhao, S.: Ionospheric electromagnetic perturbations observed on DEMETER satellite before Chile M7.9 earthquake. Earthquake Science 22(3), 251–255 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ouadfeul, SA., Tourtchine, V., Aliouane, L. (2013). Prediction of Ionospheric Perturbations Using Artificial Neural Network. Application to ISL Instrument Data- DEMETER Mission. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42054-2_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-42054-2_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42053-5
Online ISBN: 978-3-642-42054-2
eBook Packages: Computer ScienceComputer Science (R0)