Auroral electrojet predictions with dynamic neural networks
(2001) In Journal of Geophysical Research 106(A11). p.24541-24550- Abstract
- Neural networks with internal feedback from the hidden nodes to theinput [Elman, 1990 are developed for prediction of the auroralelectrojet index AE from solar wind data. Unlike linear and nonlinearautoregressive moving-average (ARMA) models, such networks are free todevelop their own internal representation of the recurrent statevariables. Further, they do not incorporate an explicit memory for paststates; the memory is implicitly given by the feedback structure of thenetworks. It is shown that an Elman recurrent network can predict around70 of the observed AE variance using a single sample of solar winddensity, velocity, and magnetic field as input. A neural network withidentical solar wind input, but without a feedback mechanism,... (More)
- Neural networks with internal feedback from the hidden nodes to theinput [Elman, 1990 are developed for prediction of the auroralelectrojet index AE from solar wind data. Unlike linear and nonlinearautoregressive moving-average (ARMA) models, such networks are free todevelop their own internal representation of the recurrent statevariables. Further, they do not incorporate an explicit memory for paststates; the memory is implicitly given by the feedback structure of thenetworks. It is shown that an Elman recurrent network can predict around70 of the observed AE variance using a single sample of solar winddensity, velocity, and magnetic field as input. A neural network withidentical solar wind input, but without a feedback mechanism, onlypredicts around 45 of the AE variance. It is also shown that fourrecurrent state variables are optimal: the use of more than four hiddennodes does not improve the predictions, but with less than that theprediction accuracy drops. This provides an indication that theglobal-scale auroral electrojet dynamics can be characterized by a smallnumber of degrees of freedom. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/130125
- author
- Gleisner, Hans and Lundstedt, Henrik LU
- organization
- publishing date
- 2001
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Ionosphere: Current systems, Ionosphere: Modeling and forecasting, Magnetospheric Physics: Solar wind/magnetosphere interactions
- in
- Journal of Geophysical Research
- volume
- 106
- issue
- A11
- pages
- 24541 - 24550
- publisher
- Wiley-Blackwell
- external identifiers
-
- scopus:39449102066
- ISSN
- 2156-2202
- DOI
- 10.1029/2001JA900046
- language
- English
- LU publication?
- yes
- id
- 4067db84-ac43-4ba1-b908-173b2a620c49 (old id 130125)
- date added to LUP
- 2016-04-04 11:18:31
- date last changed
- 2022-04-08 07:03:37
@article{4067db84-ac43-4ba1-b908-173b2a620c49,
abstract = {{Neural networks with internal feedback from the hidden nodes to theinput [Elman, 1990 are developed for prediction of the auroralelectrojet index AE from solar wind data. Unlike linear and nonlinearautoregressive moving-average (ARMA) models, such networks are free todevelop their own internal representation of the recurrent statevariables. Further, they do not incorporate an explicit memory for paststates; the memory is implicitly given by the feedback structure of thenetworks. It is shown that an Elman recurrent network can predict around70 of the observed AE variance using a single sample of solar winddensity, velocity, and magnetic field as input. A neural network withidentical solar wind input, but without a feedback mechanism, onlypredicts around 45 of the AE variance. It is also shown that fourrecurrent state variables are optimal: the use of more than four hiddennodes does not improve the predictions, but with less than that theprediction accuracy drops. This provides an indication that theglobal-scale auroral electrojet dynamics can be characterized by a smallnumber of degrees of freedom.}},
author = {{Gleisner, Hans and Lundstedt, Henrik}},
issn = {{2156-2202}},
keywords = {{Ionosphere: Current systems; Ionosphere: Modeling and forecasting; Magnetospheric Physics: Solar wind/magnetosphere interactions}},
language = {{eng}},
number = {{A11}},
pages = {{24541--24550}},
publisher = {{Wiley-Blackwell}},
series = {{Journal of Geophysical Research}},
title = {{Auroral electrojet predictions with dynamic neural networks}},
url = {{http://dx.doi.org/10.1029/2001JA900046}},
doi = {{10.1029/2001JA900046}},
volume = {{106}},
year = {{2001}},
}