A Hybrid Time Series Forecasting Model for Disturbance Storm Time Index using a Competitive Brain Emotional Neural Network and Neo-Fuzzy Neurons
Acta Polytechnica Hungarica · 2019
The Disturbance storm time (Dst) index is an important indicator of the occurrence of geomagnetic storms, which can damage communication and power systems, as well as, affect Astronauts performance.Such potential consequences of this fatal event has challenged researchers to develop Dst predictors, with some success.This paper presents the design of a computationally fast, neuro-fuzzy network to forecast Dst activity.The proposed network combines a class of emotional neural networks with neo-fuzzy neurons and is named, Neo-fuzzy integrated Competitive Brain Emotional Learning (NFCBEL) network.Equipped with five competing units, the hybrid model accepts only the past two samples of Dst time series, to predict future values.The model has been tested in the MATLAB programming environment and has been found to offer superior performance, as compared to other state-of-the-art Dst predictors.