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- W1964451330 abstract "This paper presents an application of the neuro-fuzzy modeling to analyze the time series of solar activity, as measured through the relative Wolf number. The neuro-fuzzy structure will be optimized based on the linear adapted genetic algorithm with controlling population size (LAGA-POP). First, the dimension of the time series characteristic attractor is obtained based on the smallest Regularity Criterion (RC) and the neuro-fuzzy modeling. Second, after describing the neuro-fuzzy structure and optimizing its parameters based on LAGA-POP, the performance of the present approach in forecasting yearly sunspot numbers is favorably compared to that of other published methods. Finally, the comparison predictions for the remaining part of the 22nd and the whole 23rd cycle of solar activity are presented." @default.
- W1964451330 created "2016-06-24" @default.
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- W1964451330 date "2004-09-16" @default.
- W1964451330 modified "2023-09-27" @default.
- W1964451330 title "A genetic-based neuro-fuzzy approach for prediction of solar activity" @default.
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- W1964451330 doi "https://doi.org/10.1117/12.553201" @default.
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