Solar Physics

, Volume 168, Issue 2, pp 423–433 | Cite as

Comparison of neural network and McNish and Lincoln methods for the prediction of the smoothed sunspot index

  • Françoise Fessant
  • Catherine Pierret
  • Pierre Lantos
Article
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Revised:

Abstract

In this paper we propose a comparison between two methods for the problem of long-term prediction of the smoothed sunspot index. These two methods are first the classical method of McNish and Lincoln (as improved by Stewart and Ostrow), and second a neural network method. The results of these two methods are compared in two periods, during the ascending and the declining phases of the current cycle 22 (1986–1996). The predictions with neural networks are much better than with the McNish and Lincoln method for the atypical ascending phase of cycle 22. During the second period the predictions are very similar, and in agreement with observations, when the McNish and Lincoln method is based on the data of declining phases of the cycles.

Keywords

Neural Network Classical Method Neural Network Method Current Cycle Sunspot Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Françoise Fessant
    • 1
  • Catherine Pierret
    • 2
    • 3
  • Pierre Lantos
    • 4
  1. 1.Département LAB/RIO/TNTFrance Télécom/CNETLannion CedexFrance
  2. 2.Department CT/TI/MS/MOCNESToulouse CedexFrance
  3. 3.Observatoire de MeudonMeudon CedexFrance
  4. 4.Observatoire de MeudonMeudon CedexFrance

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