global-journal-of-computer-science-and-technology-h-information-technology Global Journal of Computer Science and Technology - H: Information & Technology 0975-4350 0975-4172 Global Journals Publishing Group Incorporated 115751 Prediction of Total Electron Content using Nonlinear Autoregressive Models with eXogenous input Recurrent Neural Network KalitaDr. Santanu INDIA, Mahapurusha Srimanta Sankaradeva Viswavidyalaya 15 01 2017 17 H4

The satellite navigation system needs the prediction of the ionospheric information. It is crucial to select a competent ionospheric model and predict the value of Total Electron Content (TEC) data. Determining the TEC from dual-frequency GPS observations has become important since it is used by many researchers for different research areas. In this paper, an attempt is made for predicting TEC parameter by using Nonlinear Autoregressive models with eXogenous input recurrent neural network. The work is based on TEC data collected from the GPS receiver at Guwahati (26º 10' N, 91º 45' E).

 Full Text

The satellite navigation system needs the prediction of the ionospheric information. It is crucial to select a competent ionospheric model and predict the value of Total Electron Content (TEC) data. Determining the TEC from dual-frequency GPS observations has become important since it is used by many researchers for different research areas. In this paper, an attempt is made for predicting TEC parameter by using Nonlinear Autoregressive models with eXogenous input recurrent neural network. The work is based on TEC data collected from the GPS receiver at Guwahati (26º 10' N, 91º 45' E).