Forecasting of ionospheric critical frequency using neural networks

Date

1997-06-15

Author

Altinay, O
Tulunay, E
Tulunay, Yurdanur

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Multilayer perceptron type neural networks (NN) are employed for forecasting ionospheric critical frequency (foF2) one hour in advance. The nonlinear black-box modeling approach in system identification is used. The main contributions: 1. A flexible and easily accessible training database capable of handling extensive physical data is prepared, 2. Novel NN design and experimentation software is developed, 3. A training strategy is adopted in order to significantly enhance the generalization or extrapolation ability of NNs, 4. A method is developed for determining the relative significances (RS) of NN inputs in terms of mapping capability.

URI

https://hdl.handle.net/11511/56801

Journal

GEOPHYSICAL RESEARCH LETTERS

DOI

https://doi.org/10.1029/97gl01381

Collections

Graduate School of Natural and Applied Sciences, Article

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Citation Formats

O. Altinay, E. Tulunay, and Y. Tulunay, “Forecasting of ionospheric critical frequency using neural networks,” GEOPHYSICAL RESEARCH LETTERS, pp. 1467–1470, 1997, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56801.