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An effective neural network approach to reproduce magnetic hysteresis in electrical steel under arbitrary excitation waveforms

S. Quondam Antonio, Francesco Riganti Fulginei, Antonino Laudani, Antonio Faba, E. Cardelli

2021Journal of Magnetism and Magnetic Materials56 citationsDOI

Topics & Concepts

Preisach model of hysteresisArtificial neural networkHysteresisWaveformMagnetic hysteresisElectrical steelComputer sciencePulse-width modulationSet (abstract data type)AlgorithmControl theory (sociology)Topology (electrical circuits)PhysicsMagnetizationVoltageMaterials scienceArtificial intelligenceMagnetic fieldMathematicsCondensed matter physicsComposite materialQuantum mechanicsProgramming languageRadarControl (management)TelecommunicationsCombinatoricsMagnetic Properties and ApplicationsNon-Destructive Testing TechniquesElectric Motor Design and Analysis
An effective neural network approach to reproduce magnetic hysteresis in electrical steel under arbitrary excitation waveforms | Litcius