DocumentCode :
2231672
Title :
Neural networks in ionospheric prediction and short-term forecasting
Author :
Cander, Lj.R. ; Lamming, X.
Author_Institution :
Rutherford Appleton Lab., Chilton, UK
Volume :
2
fYear :
1997
fDate :
14-17 Apr 1997
Firstpage :
27
Abstract :
This paper discusses appropriateness of the neural network approach for ionospheric prediction and short-term forecasting. The idea of using the neural network technique to predict the future behaviour of a time series of the vertical-incidence ionospheric data has been presented and endorsed in the framework of the COST 251 project. It is based on the well-known fact that in recent years attention has turned to the application of neural network techniques to analysing and predicting the behaviour of non-linear time series. In particular, the behaviour of time series of the solar-terrestrial data such as the solar sunspot number, the solar 10.7 cm flux, and the geomagnetic activity indices
Keywords :
ionospheric electromagnetic wave propagation; COST 251 project; critical frequency; geomagnetic activity indices; ionospheric prediction; multilayer perceptron; neural networks; nonlinear time series; radiowave propagation; short-term forecasting; solar 10.7 cm flux; solar sunspot number; solar-terrestrial data; vertical-incidence ionospheric data;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Antennas and Propagation, Tenth International Conference on (Conf. Publ. No. 436)
Conference_Location :
Edinburgh
ISSN :
0537-9989
Print_ISBN :
0-85296-686-5
Type :
conf
DOI :
10.1049/cp:19970323
Filename :
606927
Link To Document :
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