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Author Makaveev, Dimitre ♦ Dupre, Luc ♦ De Wulf, Marc ♦ Melkebeek, Jan
Sponsorship (US)
Source United States Department of Energy Office of Scientific and Technical Information
Content type Text
Publisher The American Physical Society
Language English
Subject Keyword CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS ♦ FORECASTING ♦ HYSTERESIS ♦ INDUCTION ♦ MAGNETIC FIELDS ♦ NEURAL NETWORKS ♦ PERFORMANCE ♦ PHYSICS ♦ SCALARS ♦ TRAINING
Abstract A modeling technique for rate-independent (quasistatic) scalar magnetic hysteresis is presented, using neural networks. Based on the theory of dynamic systems and the wiping-out and congruency properties of the classical scalar Preisach hysteresis model, the choice of a feed-forward neural network model is motivated. The neural network input parameters at each time step are the corresponding magnetic field strength and memory state, thereby assuring accurate prediction of the change of magnetic induction. For rate-independent hysteresis, the current memory state can be determined by the last extreme magnetic field strength and induction values, kept in memory. The choice of a network training set is motivated and the performance of the network is illustrated for a test set not used during training. Very accurate prediction of both major and minor hysteresis loops is observed, proving that the neural network technique is suitable for hysteresis modeling. {copyright} 2001 American Institute of Physics.
ISSN 00218979
Educational Use Research
Learning Resource Type Article
Publisher Date 2001-06-01
Publisher Place United States
Journal Journal of Applied Physics
Volume Number 89
Issue Number 11


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