Parameter Identification of J-A Modified Model of Fe-Based Amorphous Nanocrystalline Magnetic Materials Based on Improved Gray Wolf Algorithm

For iron-based amorphous nanocrystalline magnetic materials, the shoulder area of the hysteresis loop is obviously different from materials such as silicon steel. The traditional J-A model has a poor fit for the shoulder region of the hysteresis loop. At the same time, most of the commonly used J-A parameter identification algorithms have the problems of slow convergence speed and low identification accuracy. Aiming at these problems, this paper proposes a J-A parameter identification method based on the improved gray wolf algorithm. On the basis of the traditional gray wolf algorithm, chaos initialization, dynamic weights and nonlinear convergence factors are introduced to improve the convergence speed and accuracy of the algorithm. At the same time, this method modifies the J-A model to solve the shoulder fitting problem of the hysteresis loop. The results show that the hysteresis loop obtained by this method is highly consistent with the experimentally measured hysteresis loop, and the shoulder fitting accuracy is greatly improved. This method can be used to more accurately identify the J-A model parameters of iron-based amorphous nanocrystalline magnetic materials.