학술논문

Forecasting Solar Activity With Computational Intelligence Models
Document Type
Periodical
Source
IEEE Access Access, IEEE. 6:70902-70909 2018
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Adaptive systems
Forecasting
Artificial neural networks
Predictive models
Fuzzy logic
Time series analysis
Brain modeling
Adaptive neuro-fuzzy inference system
brain emotional learning-based fuzzy inference system
computational intelligence models
solar activity forecasting
solar cycles
Language
ISSN
2169-3536
Abstract
It is vital to accurately predict solar activity, in order to decrease the plausible damage of electronic equipment in the event of a large high-intensity solar eruption. Recently, we have proposed brain emotional learning-based fuzzy inference system (BELFIS) as a tool for the forecasting of chaotic systems. The structure of BELFIS is designed based on the neural structure of fear conditioning. The function of BELFIS is implemented by assigning adaptive networks to the components of the BELFIS structure. This paper especially focuses on the performance evaluation of BELFIS as a predictor by forecasting solar cycles 16–24. The performance of BELFIS is compared with other computational models used for this purpose, in particular with the adaptive neuro-fuzzy inference system.