| Abstract: | Global temperature trends on time scales of years to centuries have recently been shown to be related to volcanic aerosols, carbon dioxide levels, and solar activity. The most visible and well-studied indicators of solar variability are dark areas or “sunspots” on the surface of the Sun, with sunspot numbers directly related to the level of solar activity. Prediction of sunspot numbers in advance of the actual event has proven problematic with most methods failing due to nonlinearities in solar activity. An approach using the generation of a feedforward neural network may resolve some of the difficulties inherent in currently utilized statistical and precursor approaches since feedforward networks offer a useful and practical method of approximating nonlinear relations and their derivatives without knowing the actual underlying nonlinear function. In this paper, we show some preliminary findings in using feedforward neural networks for the prediction of peak sunspot cycle amplitude and discuss the climatic implications of the findings. |
