One of the interesting applications is forecasting space weather phenomena, in particular solar activity and geomagnetic storms. Different methods, such as neuro-fuzzy methods and neural networks, have been examined concerning their prediction of solar cycles by using sunspot numbers or of geomagnetic storms by using disturbance time (Dst) index , auroral electrojet .(AE) index.
New connectionist approaches such as deep learning, hierarchical temporal memory and emotion-based data driven models [either ‘an emotion-based data driven model’ or ‘emotion-based data driven models’] have shown excellent results in image analysis, stock market predictions and chaotic time series prediction respectively. This session aims applying the above methods in order to predict space weather.
The main goal of this session is to:
- Evaluate the current Brain-inspired connectionist approaches on space weather prediction.
- Provide an opportunity for different researchers to share their ideas.
- Present new advances in the above approaches.