TY - JFULL AU - Salvatore Marra and Francesco C. Morabito PY - 2007/8/ TI - A New Technique for Solar Activity Forecasting Using Recurrent Elman Networks T2 - International Journal of Physical and Mathematical Sciences SP - 329 EP - 335 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/14968 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 7, 2007 N2 - In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods. ER -