论文标题
基于大脑情绪学习的预测模型，用于预测地磁风暴
Brain Emotional Learning-based Prediction Model For the Prediction of Geomagnetic Storms
论文作者
论文摘要
这项研究提出了一个新的数据驱动模型，用于预测地磁风暴。该模型是大脑情感学习启发模型（Belims）的实例，被称为基于大脑情感学习的预测模型（BELPM）。 BELPM由四个主要子系统组成；这些子系统之间的联系已被情绪系统的相应区域模仿。这些子系统的功能是使用自适应网络来解释的。 BELPM的学习算法是使用最陡峭的下降（SD）和最小平方估计器（LSE）定义的。 BELPM用于使用两个地磁指数，极光电流（AE）指数和干扰时间（DST）指数来预测地磁风暴。为了评估BELPM的性能，已将获得的结果与ANFIS，WKNN和其他Belims的实例进行了比较。结果验证了BELPM有能力为短期和长期地磁风暴预测获得合理的准确性。
This study suggests a new data-driven model for the prediction of geomagnetic storm. The model which is an instance of Brain Emotional Learning Inspired Models (BELIMs), is known as the Brain Emotional Learning-based Prediction Model (BELPM). BELPM consists of four main subsystems; the connection between these subsystems has been mimicked by the corresponding regions of the emotional system. The functions of these subsystems are explained using adaptive networks. The learning algorithm of BELPM is defined using the steepest descent (SD) and the least square estimator (LSE). BELPM is employed to predict geomagnetic storms using two geomagnetic indices, Auroral Electrojet (AE) Index and Disturbance Time (Dst) Index. To evaluate the performance of BELPM, the obtained results have been compared with ANFIS, WKNN and other instances of BELIMs. The results verify that BELPM has the capability to achieve a reasonable accuracy for both the short-term and the long-term geomagnetic storms prediction.
