Title :
A new neural-network-based scalar hysteresis model
Author :
Kuczmann, M. ; Iványi, A.
Author_Institution :
Dept. of Electromagn. Theor., Budapest Univ. of Technol. & Econ., Hungary
fDate :
3/1/2002 12:00:00 AM
Abstract :
A neural network (NN)-based model of scalar hysteresis characteristics has been developed for modeling the behavior of magnetic materials. The virgin curve and a set of the first-order reversal branches can be stored preliminary in a system of three NNs. Different properties of magnetic materials can be simulated by a simple if-then type knowledge-based algorithm. Hysteresis characteristics of different materials predicted by the introduced model are compared with the results of the classical Preisach simulation technique. Comparisons are plotted in figures
Keywords :
electromagnetic fields; feedforward neural nets; magnetic hysteresis; transfer functions; Preisach simulation technique; feedforward-type neural networks; first-order reversal branches; if-then type knowledge-based algorithm; magnetic materials; neural-network-based model; scalar hysteresis model; virgin curve; Computational modeling; Function approximation; Magnetic field measurement; Magnetic fields; Magnetic hysteresis; Magnetic materials; Magnetization; Neural networks; Predictive models; Training data;
Journal_Title :
Magnetics, IEEE Transactions on
