This paper proposes an extension to conventional regression neural networks (NNs) for
replacing the point predictions they produce with prediction intervals that satisfy a required …

Near-Earth space processes are highly nonlinear. Since the 1990s, a small group at the
Middle East Technical University in Ankara has been working on a data-driven generic …

In this paper, a neural network based regional ionospheric model is developed using GPS-
TEC data from 01 January 2012 to 31 December 2015. For this purpose, nine GPS station …

Understanding the ionospheric variability caused by the solar and geomagnetic space
weather can be achieved using the ionospheric Total Electron Content (TEC) parameter …

Statistical evaluation of the Dourbes (4.6 E, 50.1 N) digisonde automatic scaling of the more
frequently used ionospheric parameters (foF2, foF1, foE, h'F2, h'F, h'E, and M3000F2) was …

Wavelet neural networks (WNNs) are important tools for analyzing time series especially
when it is non-linear and non-stationary. It takes advantage of high resolution of wavelets …

In this paper, the modeling of the ionospheric foF 2 changes during geomagnetic storms by
means of neural network (NN) and linear regression (LR) techniques is presented. The …

The main objective of DIAS (European Digital Upper Atmosphere Server) project is to
develop a pan-European digital data collection on the state of the upper atmosphere, based …

The purpose of the LIEDR (local ionospheric electron density profile reconstruction) system
is to acquire and process data from simultaneous ground-based total electron content (TEC) …

In this paper the use of neural networks (NNs) to forecast daily hourly values of the
ionospheric F 2 layer critical frequency fo F 2, at any target geographic location up to 5 hours …