Volume 31 Issue 2
Mar.  2011
Turn off MathJax
Article Contents
Chen Chun, Sun Shuji, Xu Zhengwen, Zhao Zhenwei, Wu Zhensen. Index Forecasting Method Based on Neural Network Techniques[J]. Chinese Journal of Space Science, 2011, 31(2): 182-186. doi: 10.11728/cjss2011.02.182
Citation: Chen Chun, Sun Shuji, Xu Zhengwen, Zhao Zhenwei, Wu Zhensen. Index Forecasting Method Based on Neural Network Techniques[J]. Chinese Journal of Space Science, 2011, 31(2): 182-186. doi: 10.11728/cjss2011.02.182

Index Forecasting Method Based on Neural Network Techniques

doi: 10.11728/cjss2011.02.182
More Information
  • Corresponding author: Chen Chun
  • Received Date: 1900-01-01
  • Rev Recd Date: 1900-01-01
  • Publish Date: 2011-03-15
  • Magnetic storm has always been one of the key issues of solar-terrestrial space physics for the past half century. The Dst index is the longitudinally averaged magnetic field depression at low latitudes. It is the primary measure of the magnitude of magnetic storms, and provides a convenient way to monitor the magnetospheric ring current. By using artificial neural network and considering the effects of the period of the geomagnetic activity, this paper brought out a method of forecasting Dst index, an hour in advance. The inputs of the network include time, season, Dst index and the first difference and the second difference of Dst index, the mean value of 27 days ago at t time, and the output is the observed Dst index data at next time. The trained net then can forecast Dst index 1 h ahead. Some examples are presented by using the Dst index data in 1985, 1986, 1990, 1991, respectively. The results indicate that the predicted Dst index has good agreement with observed data and the corresponding root mean errors of the model comparing with the measurement were 4.00 nT, 3.72 nT, 5.35 nT and 6.82 nT, respectively. In addition, the error analysis indicates that the predicted root-mean-square error of Dst index is smaller in low solar activities than in high solar activities.

     

  • loading
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article Views(2203) PDF Downloads(997) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return