Data Driven Function Approximation Models For Simulating Magnetic Hysteresis

We investigate several data driven function approximation models to learn the hysteretic behavior of ferromagnetic materials. They include neural networks, linear least squares models based on radial basis functions, and low order polynomial functions. When properly trained, they are capable of predicting major features of magnetization and torque loops. (C) 2002 American Institute of Physics.