Prediction of solar cycle 24

Accurate prediction of solar activity as one aspect of space weather phenomena is essential to decrease the damage from these activities on the ground based communication, power grids, etc. Recently, the connectionist models of the brain such as neural networks and neuro-fuzzy methods have been prop...

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Bibliographic Details
Main Authors: Parsapoor, Mahboobeh, Bilstrup, Urban, Svensson, Bertil
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Brain
brain emotional learning-based recurrent fuzzy system
emotional system
Extraterrestrial measurements
Fuzzy logic
Mathematical models
Neural networks
Solar activity
solar activity forecasting
Solar cycles
Space weather
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Summary:Accurate prediction of solar activity as one aspect of space weather phenomena is essential to decrease the damage from these activities on the ground based communication, power grids, etc. Recently, the connectionist models of the brain such as neural networks and neuro-fuzzy methods have been proposed to forecast space weather phenomena; however, they have not been able to predict solar activity accurately. That has been a motivation for the development of the connectionist model of the brain; this paper aims to apply a connectionist model of the brain to accurately forecasting solar activity, in particular, solar cycle 24. The neuro-fuzzy method has been referred to as the brain emotional learning-based recurrent fuzzy system (BELRFS). BELRFS is tested for prediction of solar cycle 24, and the obtained results are compared with well-known neuro-fuzzy methods and neural networks as well as with physical-based methods.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2015.7280839