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References

  • Altinay, O., E. Tulunay, and Y. Tulunay (1997), Forecasting of ionospheric critical frequency using neural networks, Geophys. Res. Lett., 24, 14671470.
  • Bilitza, D. (2000), The importance of EUV indices for the International Reference Ionosphere, Phys. Chem. Earth, Part C, 25, 515521.
  • Bradley, P. A. (1993), Indices of ionospheric response to solar-cycle epoch, Adv. Space Res., 13, 2528.
  • Bradley, P. A. (Ed.) (1995), COST 238-PRIME (Prediction and Retrospective Ionospheric Modelling over Europe), final report, advance issue, Eur. Comm., Brussels, Oct.
  • Cander, L. R., and X. Lamming (1997), Neural networks in ionospheric prediction and short-term forecasting, IEEE Conf. Publ., 496, 2.272.30.
  • Fausett, L. (1994), Fundamentals of Neural Networks, Prentice-Hall, Upper Saddle River, N. J.
  • Haykin, S. (1994), Neural Networks: A Comprehensive Foundation, Macmillan, New York.
  • Kouris, S. S., P. A. Bradley, and P. Dominici (1998), Solar-cycle variation of the daily foF2 and M(3000)F2, Ann. Geophys., 16, 10391042.
  • Kumluca, A., E. Tulunay, and I. Topalli (1999), Temporal and spatial forecasting of ionospheric critical frequency using neural networks, Radio Sci., 34, 14971506.
  • Lamming, X., and L. R. Cander (1999), Monthly median foF2 modelling COST 251 area by neural networks, Phys. Chem. Earth, Part C, 24, 349354.
  • Liu, J. Y., Y. I. Chen, and J. S. Lin (2003), Statistical investigation of the saturation effect in the ionospheric foF2 versus sunspot, solar radio noise, and solar EUV radiation, J. Geophys. Res., 108(A2), 1067, doi:10.1029/2001JA007543.
  • McKinnell, L. A. (1996), A new empirical model for the peak ionospheric electron density using neural networks, MSc. thesis, Rhodes Univ., Grahamstown. S. Afr.
  • McKinnell, L. A., and A. W. V. Poole (2000), The development of a neural network based short-term foF2 forecast program, Phys. Chem. Earth, Part C, 25, 287290.
  • Oyeyemi, E. O., and A. W. V. Poole (2004), Towards the development of a new global foF2 empirical model using neural networks, Adv. Space Res., 34, 19661972.
  • Oyeyemi, E. O., A. W. V. Poole, and L. A. McKinnell (2005), On the global model for foF2 using neural networks, Radio Sci., 40, RS6011, doi:10.1029/2004RS003223.
  • Poole, A. W. V., and L. A. McKinnell (2000), On the predictability of foF2 using neural networks, Radio Sci., 35, 225234.
  • Richard, S., et al. (2004), Nowcasting, forecasting and warning for ionospheric propagation: Tools and methods, Ann. Geophys., 47, 957983.
  • Rush, C. M. (1975), An ionospheric observation network for use in short-term propagation predictions, Telecommun. J., 43, 544549.
  • Sethi, N. K., M. K. Goel, and K. K. Mahajan (2002), Solar cycle variations of foF2 from IGY to 1990, Ann Geophys., 20, 16771685.
  • Tulunay, E., C. Özkaptan, and Y. Tulunay (2000), Temporal and spatial forecasting of the foF2 values up to twenty four hours in advance, Phys. Chem. Earth, Part C, 25, 281285.
  • Williscroft, L. A., and A. W. V. Poole (1996), Neural networks, foF2, sunspot and magnetic activity, Geophys. Res. Lett., 23, 36593662.
  • Wintoft, P. (2000), Twenty-four hour predictions of foF2 using neural networks, Radio Sci., 35, 395408.
  • Wintoft, P., and L. R. Cander (1999), Short-term prediction of foF2 using time delay neural networks, Phys. Chem. Earth, Part C, 24, 343347.
  • Wintoft, P., and L. R. Cander (2000), Ionospheric foF2 storm forecasting using neural networks, Phys. Chem. Earth, Part C, 25, 267273.