A factor analysis and neural network-based validation of the Varotsos-Cracknell theory on the 11-year solar cycle

  • Authors:

  • Surajit Chattopadhyay;Goutami Chattopadhyay

  • Affiliations:

  • Department of Information Technology, Pailan College of Management and Technology, Affiliated to West Bengal University of Technology, Bengal Pailan Park, Kolkata 700 104, India;1/19 Dover Place, Kolkata 700 019, Formerly, Department of Atmospheric Sciences, University of Calcutta, Kolkata 700 019, India

  • Venue:
  • International Journal of Remote Sensing - The Remote Sensing Heritage of Academician Kirill Ya Kondratyev
  • Year:
  • 2008

  • Preface

    International Journal of Remote Sensing - The Remote Sensing Heritage of Academician Kirill Ya Kondratyev

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Abstract

The validation of the recently proposed Guttenberg-Richter law in the 11-year solar cycle has been attempted in this paper through the employment of an Artificial Neural Network (ANN) in the form of the Multilayer Perceptron (MLP). The 11-year solar cycle has been reviewed using the autocorrelation function. Factor analysis has been attempted to identify the most important predictors for the ANN-based prediction of yearly sunspot numbers. Solar cycle length has been predicted through ANN, and its predictive ability has been examined by comparing with regression approaches. After rigorous study ANN has been found to be more efficient than the regression approach in predicting the solar cycle length. Finally, the existence of the Guttenberg-Richter law in the solar cycle has been validated by ANN.