Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
Local Linear Model Trees for On-Line Identification of Time-Variant Nonlinear Dynamic Systems
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
A High-Performance Framework for Sun-to-Earth Space Weather Modeling
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 13 - Volume 14
Predicting Chaotic Time Series Using Neural and Neurofuzzy Models: A Comparative Study
Neural Processing Letters
Extracting the main patterns of natural time series for long-term neurofuzzy prediction
Neural Computing and Applications
A new systematic design for Habitually Linear Evolving TS Fuzzy Model
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Research in space weather has in recent years become an active field of research requiring international cooperation because of its importance in hazard warning especially for satellite technology and power utility systems. The time-varying sun as the main source of space weather impacts the Earth's magnetosphere by emitting hot magnetized plasma called solar wind into interplanetary space. The emission of Solar Energetic Particles (SEPs) and consequently the magnitude of Interplanetary Magnetic Field (IMF) vary almost periodically with an approximate life cycle of 11years. It is shown that the solar and geomagnetic activity indices have complex behavior often characterizable as quasi-periodic or even chaotic, which causes the long-term prediction to be a conundrum. Moreover, solar and geomagnetic activity indices and their chaotic characteristics vary abruptly during solar and geomagnetic storms. This variation depicts the difficulties in modeling and long-term prediction of solar and geomagnetic storms. On the other hand, neural networks and related neurofuzzy tools as general function approximators have been the subjects of interest due to their many practical applications in modeling and predicting complex phenomena. However, most of these systems are trained by algorithms that need to be carried out by an off-line data set which influence their performance in prediction of time-varying solar and geomagnetic activity indices. This paper proposes an adaptive neurofuzzy approach with a recursive learning algorithm for modeling and prediction of space weather indices which fulfill requirements of prediction of time-varying solar and geomagnetic activities for long time spans. The obtained results depict the power of the proposed method in online prediction of time-varying solar and geomagnetic activity indices.