We propose a method, based on Neural Networks, that detects the nonlinear robust interplanetary solar wind variables, with varying delays, driving the coupled behavior of three geomagnetic indices (Dst, AL, and AU). As opposed to minimizing a prediction error, the method is based on degrading the prediction by distorting the inputs of the trained Neural Networks in order to highlight the most sensible drivers. We show that the z component of the magnetic field, the duskward oriented electric field, and the speed of the particles of the interplanetary medium, at particular time delays, seem to be the most efficient drivers of the three coupled geomagnetic indices. Using only the sensible or robust drivers in the model, we demonstrate that iterated predictions during geomagnetic storm are significantly improved from models that only use one of the outstanding drivers with multiple time delays. The derived robust nonlinear Neural Network model is also a significant improvement over linear approximations, specially when used as iterated predictors.

中文翻译：

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一种非线性系统科学方法，用于寻找多变量磁层的稳健太阳风驱动器

我们提出了一种基于神经网络的方法，该方法检测非线性鲁棒行星际太阳风变量，具有不同的延迟，驱动三个地磁指数（Dst、AL 和 AU）的耦合行为。与最小化预测误差相反，该方法基于通过扭曲受过训练的神经网络的输入来降低预测，以突出最明智的驱动程序。我们证明z磁场的分量、暗方向电场和行星际介质粒子的速度，在特定的时间延迟，似乎是三个耦合地磁指数的最有效驱动因素。仅使用模型中合理或稳健的驱动程序，我们证明了地磁风暴期间的迭代预测比仅使用具有多个时间延迟的优秀驱动程序之一的模型显着改善。派生的鲁棒非线性神经网络模型也是对线性近似的显着改进，特别是在用作迭代预测器时。