Predicting Astronaut Radiation Doses From Large Solar Particle Events Using Artificial Intelligence 1999-01-2172
For deep space missions, a major concern is the occurrence of large solar particle events. In this work a dynamic, new type of artificial neural network called a Sliding Time Delay Neural Network that is capable of accurately predicting total dose for an event, from several input doses early in the event, is presented. The network can update its total dose predictions during the event as new input data are received. Results from testing indicate that the network can predict total doses from large events that are outside the training set to within 4% very early in the event.
Citation: Tehrani, N., Townsend, L., Hines, J., and Forde, G., "Predicting Astronaut Radiation Doses From Large Solar Particle Events Using Artificial Intelligence," SAE Technical Paper 1999-01-2172, 1999, https://doi.org/10.4271/1999-01-2172. Download Citation
Author(s):
Nazila H. Tehrani, Lawrence W. Townsend, J. Wesley Hines, Garth M. Forde
Affiliated:
The University of Tennessee
Pages: 6
Event:
International Conference On Environmental Systems
ISSN:
0148-7191
e-ISSN:
2688-3627
Related Topics:
Neural networks
Artificial intelligence (AI)
Particulate matter (PM)
Sun and solar
Radiation
Education and training
SAE MOBILUS
Subscribers can view annotate, and download all of SAE's content.
Learn More »
