6533b7d3fe1ef96bd125feb1
RESEARCH PRODUCT
Dynamic Preisach Hysteresis Model for Magnetostrictive Materials for Energy Application
Vincenzo FranzittaG. La RoccaAlessia ViolaMarco Trapanesesubject
Electric machinePreisach model of hysteresisEngineeringbusiness.product_categoryArtificial neural networkbusiness.industryMagnetostrictionGeneral MedicinePerceptronHysteresisTransducerControl theorybusinessEnergy (signal processing)description
In this paper the magnetostrictive material considered is Terfenol-D. Its hysteresis is modeled by applying the DPM whose identification procedure is performed by using a neural network procedure previously publised [. The neural network used is a multiplayer perceptron trained with the Levenberg-Marquadt training algorithm. This allows to obtain the Preisach distribution function, without any special conditioning of the measured data, owing to the filtering capabilities of the neural network interpolators. The model is able to reconstruct both the magnetization relation and the Field-strain relation. The model is validated through comparison and prediction of data collected from a typical Terfenol-D transducer.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2013-09-01 | Applied Mechanics and Materials |