A NEURAL-NETWORK MODEL OF PARAMETRIC NONLINEAR HYSTERETIC INDUCTORS

A neural network model of non-linear hysteretic inductors is presented . The proposed neural network model is shown to reproduce the dynamic scalar hysteretic behavior proper of the current-flux relationship. Mo reover, the intrinsic characteristics of the neural network approach y ield the model particularly suitable when dependencies on different pa rameters are present, e.g., influence of mobile part positions, of tem perature etc.. Both theoretical comparisons (e.g., Chua-Stronsmoe mode l and Jiles-Atherton model) and experimental measurements (e.g,, varia ble reluctance linear motor) are considered. Results point out the num erical accuracy and computational efficiency of the proposed NN approa ch, that results in a general framework useful in the field of electri cal machinery, of electronic power circuits, of control and identifica tion.