Modeling D st  with Recurrent EM Neural Networks

Author:

Mirikitani Derrick Takeshi,Ouarbya Lahcen

Publisher

Springer Berlin Heidelberg

Reference18 articles.

1. Axford, W.I., Hines, C.O.: A unifying theory of high-latitude geophysical phenomena and geomagnetic storms. Can. J. Phys. 39, 1433–1464 (1961)

2. de Freitas, J.F.G., Niranjan, M., Gee, A.H.: Dynamic Learning with the EM Algorithm for Neural Networks. J. Vlsi. Signal. Proc. 26, 119–131 (2000)

3. Dungey, J.W.: Interplanetary magnetic field and the auroral zones. Phys. Rev. Lett. 26, 47–48 (2000)

4. Freeman, J., Natal, A., Reiff, P., Denig, W., Gussenhoven-Shea, S., Heinemann, M., Rich, F., Hairston, M.: The use of neural networks to predict magnetospheric parameters for input to a magnetospheric forecast model. In: Proceedings of Artificial Intelligence Applications in Solar-Terrestrial Physics Workshop, pp. 167–181 (1993)

5. Gonzales, W.D., Joselyn, J.A., Kamide, Y., Kroehl, H.W., Rostoker, G., Tsurutani, B.T., Vasyliunas, V.M.: What is a geomagnetic storm? J. Geophys. Res. 99, 5771–5792 (1994)

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