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Prediction of Electroencephalogram Time Series via Artificial Neuro-Fuzzy Inference System Trained by League Championship Algorithm
Abstract
Time series analysis has a wide application interest in artificial-intelligence-oriented research studies. Because it is easy to run machine-learning-based solutions directly over time series, it has been a popular approach to use alternative types of intelligent systems to analyze time series. Regarding such works, time series prediction is known as a remarkable topic as followed by researchers from different fields. The objective of this chapter is to provide an alternative work by using artificial neuro-fuzzy inference system trained by the league championship algorithm, which is an optimization algorithm from the associated literature. As the application objective, electroencephalogram (EEG) time series have been tried to be predicted by using the designed ANFIS-LCA approach. The chapter briefly introduces details about the approach and reports findings from the performed prediction operations.
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