A neural network model of the relativistic electron flux at geosynchronous orbit

Details A neural network model of the relativistic electron flux at geosynchronous orbit A neural network model of the relativistic electron flux at geosynchronous orbit

Apr 1991

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1991jgr....96.5549k&link_type=abstract

Journal of Geophysical Research (ISSN 0148-0227), vol. 96, April 1, 1991, p. 5549-5556.

Other

32

Earth Orbital Environments, Geomagnetism, Neural Nets, Relativistic Electron Beams, Solar Terrestrial Interactions, Geosynchronous Orbits, International Sun Earth Explorers, Magnetic Storms

Scientific paper

A neural network has been developed to model the temporal variations of relativisitic electron flux at geosynchronous orbit based on model inputs consisting of 10 consecutive values of the daily-summed planetary goemagnetic index. The network provides results which are significantly more accurate than linear prediction filters. The model can be used to infer geosynchronous electron fluxes for periods in which direct measurements are not available or are contaminated by background from solar proton events. It has direct applicabilty to the analysis of satellite anomalies which are thought to be due to the deep dielectric charging process. The model also provides a simple and accurate framework for studying other aspects of the behavior of the geosynchronous electron environment, including its dependence on the solar cycle and the relative phase of Jupiter. It provides a capability for simulating conditions which rarely occur in nature, such as prolonged steady state conditions or discrete impulse responses.

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