基于BP神经网络的地磁变化场预测研究

以现有地磁台站的长期海量观测数据为基础，忽略地磁场成因及众多影响因素的复杂作用机理，对地磁变化场的时空关联性进行分析，挖掘数据中蕴含的规律信息，进一步构建基于BP神经网络的地磁变化场预测模型。通过实际地磁台站的观测数据对模型预测结果进行验证，结果表明，对于任意选取的100组验证数据，均方根误差为4.8 nT，求解精度能够满足一般科学研究对地磁变化场的精度需求。

Forecasting of the Variable Geomagnetic Field Based on BP Neural Network

Based on the massive long-term observation data from existing geomagnetic stations,this paper disregards the formation of geomagnetic field and the complex mechanisms of many influencing factors,and analyzes the temporal and spatial correlation of the variable geomagnetic field,mining the regular information contained in the data.It further constructs the prediction model of geomagnetic field based on the BP neural network.The forecasting data of the established model is validated by the observation data of geomagnetic stations,and the result shows that root mean squared error of the 100 arbitrarily chosen validation data is 4.8 nT,and this solution accuracy can meet the need of general scientific research.