Prediction of magnetospheric parameters using artificial neural networks

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dc.creatorNagai, Akiraen_US
dc.date.accessioned2007-08-21T01:43:11Zen_US
dc.date.available2007-08-21T01:43:11Zen_US
dc.date.issued1994en_US
dc.description.abstractArtificial neural network models have been developed that provide the magnetospheric parameters Dst, polar cap potential and the midnight equatorward boundary of diffuse aurora. Layered feedforward neural networks have successfully learned the relationship between the solar wind and the magnetospheric parameters using supervised back-propagation training. All models have achieved a higher prediction accuracy than the existing empirical or statistical models. These models are applied to the prediction of the parameters, which will then be used by the Rice Magnetospheric Specification and Forecast Model (MSFM). The neural network models are able to forecast the magnetospheric parameters 30 to 60 minutes ahead using the information from a solar wind monitor spacecraft. With the forecast values, the MSFM will be able to forecast particle fluxes in the inner magnetosphere. The MSFM is applied to the April 1988 magnetic storm for the forecast capability test. The neural network modeling, the comparison of the prediction accuracy with other methods and the result of the MSFM forecast capability test are presented.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.callnoTHESIS SP. SCI. 1994 NAGAIen_US
dc.identifier.citationNagai, Akira. "Prediction of magnetospheric parameters using artificial neural networks." (1994) Diss., Rice University. <a href="https://hdl.handle.net/1911/19091">https://hdl.handle.net/1911/19091</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/19091en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectStatisticsen_US
dc.subjectPhysicsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectComputer scienceen_US
dc.titlePrediction of magnetospheric parameters using artificial neural networksen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentSpace Scienceen_US
thesis.degree.disciplineNatural Sciencesen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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