Modeling of mean-field ising-hysteresis behavior: A support vector machine classification

Abstract

Ferromagnetic hysteresis behavior is a lagging relation between magnetization and external magnetic field. This understanding is useful for designing highly efficient magnetic applications e.g., magnetic recording devices. A magnetic phase and hysteresis properties fluctuate clearly when encountered with thermal noise. This creates difficulties in predicting and modeling; and poses a very challenging problem. In this study, we propose to fit parameters and select the suitable kernel functions of Support Vector Machine, of which the main tasks are i) managing the relationship among hysteresis properties, temperature, magnetic field, and magnetic frequency, and ii) classifying the symmetries of hysteresis. The results present a new novel of classifying symmetric behavior of hysteresis with high accuracy. © 2014 Taylor & Francis Group, LLC.