Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting

Abstract

Accurate prediction of total electron content (TEC) is important for monitoring the behavior of the ionosphere and indeed a magnitude of interest to understand the properties and behavior of the Sun–Earth System. The conditions of this medium have a direct impact on a growing variety of critical technological infrastructure. This work presents a comparison between two different artificial neural networks (ANNs): an adaptive neuro-fuzzy inference system and nonlinear autoregressive neural network (NAR-NN) applied to TEC. Both ANNs where tested on four different geomagnetic locations on 4 1-week periods having a variety of geomagnetic disturbance levels. The effect of using different training period lengths and the system response for 60 and 30 min sampling rate TEC time series was investigated. NAR-NN shows a slightly better performance, being the higher difference during the greater perturbations. There is also a better response when sampling rates of 30 min are used.