Search results for "Preisach model of hysteresis"
showing 5 items of 5 documents
Dynamic Preisach Hysteresis Model for Magnetostrictive Materials for Energy Application
2013
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 …
Magnetic Stochastic Resonance in systems described by Dynamic Preisach Model
2008
Stochastic resonance (SR) is generally considered as an enhancement of the system response for certain finite values of the noise strength. In particular the signal to noise ratio (SNR) and the signal amplification show a maximum as a function of the noise intensity. This effect has been experimentally observed in many physical systems and also in magnetic systems. However, as far as magnetic systems are concerned, the dynamic features of the systems have been neglected and it has been assumed that the typical relaxation time is negligible. However this is clearly a rough approximation. In order to clarify this relation, in this paper we numerically study magnetic stochastic resonance in se…
A Novel Neural Approach to the Determination of the Distribution Function in Magnetic Preisach Systems
2004
This paper presents a novel method to identify both the functional dependence of the Preisach function as well as its numerical parameters on the basis of some known magnetic behavior. In this paper, the identification of the Preisach function of a material is performed by using a neural network trained by a collection of hysteresis curves, whose Preisach functions are known. When a new hysteresis curve is given as input to this neural network, it is able to give as output both the functional dependence of the Preisach function as well as its numerical parameters.
State-space formulation of scalar Preisach hysteresis model for rapid computation in time domain
2015
A state-space formulation of classical scalar Preisach model (CSPM) of hysteresis is proposed. The introduced state dynamics and memory interface allow to use the state equation, which is rapid in calculation, instead of the original Preisach equation. The main benefit of the proposed modeling approach is the reduced computational effort which requires only a single integration over the instantaneous line segment in the Preisach plane. Numerical evaluations of the computation time and model accuracy are provided in comparison to the CSPM which is taken as a reference model.
On the dependence of magnetic stochastic resonance features on the features of magnetic hysteresis
2005
Numerical study of magnetic stochastic resonance (SR) in several magnetic systems having different hysteresis loops was performed. The various hysteresis loops were modeled by using Preisach model in which several identification functions were used. The results showed the dependence of SR on the parameters of Preisach function. The results also showed how the field H/sub 0/ shifted the onset of SR and how a large dispersion of the distribution of hysterons degraded the SR.