• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Multi-Conference on Systems, Signals and Devices >Support Vector Machines for Automated Knowledge Extraction from Historical Solar Data: A Practical Study on CME Predictions
【24h】

Support Vector Machines for Automated Knowledge Extraction from Historical Solar Data: A Practical Study on CME Predictions

机译：支持历史太阳能数据的自动知识提取的向量机：CME预测的实践研究

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, Associations algorithms and Support Vector Machines (SVM) are applied to analyse years of solar catalogues data and to study the associations between eruptive filaments/prominences and Coronal Mass Ejections (CMEs). The aim is to identify patterns of associations that can be represented using SVM learning rules to enable real-time and reliable CME predictions. The NGDC filaments catalogue and the SOHO/LASCO CMEs catalogue are processed to associate filaments with CMEs based on timing and location information. Automated systems are created to process and associate years of filaments and CME data, which are later arranged in numerical training vectors and fed to machine learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Features representing the filament time, duration, type and extent are extracted from all the associated (A) and not-associated (NA) filaments and converted to a numerical format that is suitable for machine learning use. The machine learning system predicts if the filament is likely to initiate a CME. Intensive experiments are carried out to optimise the SVM. The prediction performance of SVM is analysed and recommendations for enhancing the performance are provided.
机译：在本文中，应用关联算法和支持向量机（SVM）来分析多年的太阳能目录数据，并研究爆发细丝/突出和冠状质量喷射（CMES）之间的关联。目的是识别可以使用SVM学习规则来表示的关联模式，以实现实时和可靠的CME预测。 NGDC细丝目录和SOHO / Lasco CMES目录被处理以基于定时和位置信息将具有CME的细丝与CME相关联。创建自动化系统以处理和关联多年的细丝和CME数据，后来排列在数值训练向量中，并馈送到机器学习算法以提取嵌入式知识并提供可用于CME自动预测的学习规则。表示灯丝时间，持续时间，类型和范围的特征是从所有相关的（a）和未关联的（na）细丝中提取并转换为适合于机器学习使用的数字格式。机器学习系统预测灯丝可能发起CME。进行了密集实验以优化SVM。分析了SVM的预测性能，并提供了提高性能的建议。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号