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Verdes P.F. Parodi M.A. Granitto P.M. Navone H.D. Piacentini R.D. Ceccatto H.A. 《Solar physics》2000,191(2):419-425
Two nonlinear methods are employed for the prediction of the maximum amplitude for solar cycle 23 and its declining behavior. First, a new heuristic method based on the second derivative of the (conveniently smoothed) sunspot data is proposed. The curvature of the smoothed sunspot data at cycle minimum appears to correlate (R 0.92) with the cycle's later-occurring maximum amplitude. Secondly, in order to predict the near-maximum and declining activity of solar cycle 23, a neural network analysis of the annual mean sunspot time series is also performed. The results of the present study are then compared with some other recent predictions. 相似文献
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The sunspot record of solar magnetic activity is studied as a nonstationary time series by means of a previously developed
algorithm for treating perturbed dynamical systems. This approach incorporates secular changes into the modeling process through
an external driving parameter, whose temporal behavior is shown to correspond in this case to the long-term trend of the sunspot
record. Our method is able to reduce by approximately 13% the prediction error of this series when compared to the standard
stationary approach. Such a reduction is remarkable in view of the benchmark status of the sunspot record in the statistical
literature and, moreover, the fact that this gain is obtained over the performance of an already very competitive modeling
technique based on ensembles of artificial neural networks. 相似文献
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Predicting Indian monsoon rainfall: a neural network approach 总被引:5,自引:0,他引:5
The summer monsoon rainfall over India is predicted by using neural networks. These computational structures are used as a nonlinear method to correlate preseason predictors to rainfall data, and as an algorithm for reconstruction of the rainfall time-series intrinsic dynamics. A combined approach is developed which captures the information built into both the stochastic approach based on suitable predictors and the deterministic dynamical model of the time series. The hierarchical network so obtained has forecasting capabilities remarkably improved with respect to conventional methods. 相似文献
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