基于Elman神经网络的磁场探头校准建模

Magnetic field probe calibration modeling based on Elman neural network

针对大多数磁场探头都是在频域上校准、不能较好地满足测量电磁瞬变现象的问题,提出了一种基于Elman神经网络的磁场探头时域校准建模方法。利用Helmholtz线圈、浪涌发生器、示波器等仪器搭建了时域校准平台,对磁场探头进行校准实验,并采集浪涌发生器输出的电流波形和磁场探头的感应电压。分别以磁场探头的感应电压和Helmholtz线圈产生的磁感应强度数据作为输入输出,建立Elman神经网络模型。建模效果表明,所建模型能够准确地预测出磁感应强度的变化趋势,该校准模型具有一定有效性。

For most of the magnetic field probes were calibrated in the frequency-domain and could not satisfy the measurement requirement of electromagnetic transient,this paper proposes a time-domain calibration method based on Elman neural network.The method uses Helmholtz coils,surge generators,oscilloscopes and other instruments to build a time-domain calibration platform,and collects the output current of surge generator and the induced voltage of magnetic field probe.The induced voltage and the magnetic induction intensity are taken as the input and output data,then the Elman neural network model is built.The results show that,the model can accurately predict the change tendency of the magnetic induction intensity and the calibration model is effective.