A neuro-fuzzy-based Preisach approach on hysteresis modeling

Most of the present identification techniques of the Preisach model rely on a fairly large amount of experimental data. This paper proposes a method that utilizes the available data in the major loop and omits the need of measuring the first-order reversal curves. The method models hysteresis by applying the classical Preisach model whose identification procedure is performed by the utility of neuro-fuzzy approximators. The method is applied to the prediction of cyclic minor loops of a soft magnetic composite. The simulation and measurement results show the good accuracy of the method and validate the proposed approach.