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 …

This paper is a final review of the Simplified Ionospheric Regional Model (SIRM) developed
as a prototype in the early 1990s and improved in the following years. By means of an …

The total electron content (TEC) is one of the most important parameters for studying the
behavior of the ionosphere. The global ionosphere maps (GIMs) can be used to study the …

We propose using the generalized regression neural network (GRNN) method for spatio-
temporal modeling of ionosphere total electron content (TEC). The GRNN model uses radial …

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 …

Given the severe effects of the ionosphere on global navigation satellite system (GNSS)
signals, single-frequency (SF) precise point positioning (PPP) users can only achieve …

One of the major research areas in the space weather community is the ability to
understand, characterize, and model a time‐space variant ionosphere through which …

This paper proposes a linear time series model to represent the climatology of the
ionosphere and to investigate the characteristics of hourly averaged total electron content …

In this study, a 3D-model of the electron density has been performed using the global
positing system (GPS) measurements over Iran. 2D spherical harmonic functions and …

We examine systematic differences between topside electron density measurements and
different topside model formulations including α‐Chapman, NeQuick, and an improved …