Research on the inverse vector hysteresis model with the deep learning parameter identification algorithm

•Based on the white-box model theory, the Stacked Auto-Encoder is used to identify the relevant parameters of this model.•The anisotropy compensation coefficient is introduced into the model structure to reduce the model error.•The phase angle compensation parameters are calculated by the magnetic loss data to improve the loss calculation accuracy.•The Neural Networks construct the mapping between model structure and the output of hysteresis data.•The median iterative algorithm is used to search the corresponding compensation angle values.