Stroke-dependent magnetic hysteresis modeling in proportional solenoids using parametric Gaussian-mixture Preisach distribution

2014 ◽  
Author(s):  
Michael Ruderman
Keyword(s):  
Magnetic Hysteresis ◽  
Gaussian Mixture ◽  
Hysteresis Modeling

scholarly journals Implementation of Preisach-Krasnoselskii Hysteresis Model with the Use of Artificial Neural Networks

2021 ◽  
Author(s):  
Zhao Wang
Keyword(s):  
Neural Networks ◽  
Magnetic Hysteresis ◽  
Feedforward Neural Networks ◽  
Hysteresis Model ◽  
Electromagnetic Devices ◽  
Hysteresis Modeling ◽  
Simulation Results ◽  
And Performance ◽  
Hysteresis Phenomena ◽  
Double Integrals

Accurate modeling of hysteresis is essential for both the design and performance evaluation of electromagnetic devices. This project proposes the use of feedforward meural networks to implement an accurate magnetic hysteresis model based on the mathematical difinition provided by the Preisach-Krasnoselskii (P-K) model. Feedforward neural networks are a linear association networks that relate the ouput patterns to input patterns. By introducing the multi-layer feedforward neural networks make the hysteresis modeling accurate without estimation of double integrals. Simulation results provide the detailed illustrations. The comparisons with the experiments show that the proposed approach is able to satisfactorily reproduce many features of obsereved hysteresis phenomena an in turn can be used for many applications of interest.

Magnetic Hysteresis Modeling of Giant Magnetoimpedance Thin Films

2007 ◽  
Vol 5 (1) ◽  
pp. 133-137 ◽  
Author(s):  
H. Hauser ◽  
I. Giouroudi ◽  
J. Steurer ◽  
L. Musiejovsky ◽  
J. Nicolics
Keyword(s):  
Thin Films ◽  
Magnetic Hysteresis ◽  
Giant Magnetoimpedance ◽  
Hysteresis Modeling

Thermal effects on magnetic hysteresis modeling

2012 ◽  
Vol 61 (1) ◽  
pp. 77-84 ◽  
Author(s):  
A. Ladjimi ◽  
M. Mékideche ◽  
A. Babouri
Keyword(s):  
Thermal Effects ◽  
Magnetic Hysteresis ◽  
Hysteresis Loops ◽  
Magnetic Hysteresis Loop ◽  
Effect Of Temperature ◽  
Temperature Dependent ◽  
Ferrite Material ◽  
Hysteresis Modeling ◽  
Dependent Model ◽  
Temperature Dependent Model

Thermal effects on magnetic hysteresis modelingA temperature dependent model is necessary for the generation of hysteresis loops of ferromagnetic materials. In this study, a physical model based on the Jiles-Atherton model has been developed to study the effect of temperature on the magnetic hysteresis loop. The thermal effects were included through a model of behavior depending on the temperature parametersMsandkof the Jiles-Atherton model. The temperature-dependent Jiles-Atherton model was validated through measurements made on ferrite material (3F3). The results have been found to be in good agreement with the model.

Optimization of Distribution Functions for the Hysteresis Preisach Model by Genetic Algorithms

2015 ◽  
Vol 799-800 ◽  
pp. 1330-1338
Author(s):  
Mounir Boudjerda ◽  
Mounir Amir ◽  
Mourad Zergoug ◽  
Siham Azzi ◽  
Mouhamed Sahnoun
Keyword(s):  
Genetic Algorithm ◽  
Hysteresis Loop ◽  
Magnetic Hysteresis ◽  
Ferromagnetic Material ◽  
Distribution Functions ◽  
Preisach Model ◽  
Wide Range ◽  
Material Behaviors ◽  
Hysteresis Modeling

The description of hysteresis is one of the classical problems in magnetic materials. The progress in its solution determines the reliability of modeling and the quality of design of a wide range of contemporary devices, as well as devices that will be created in the future. The intensive investigations in hysteresis modeling were induced by the fact that accuracy models of magnetic hysteresis must be studied yet. In this paper, several identification procedures of the distribution functions of the Preisach model will be investigated by means of a genetic algorithm.The proposed approach has been applied to model the behavior of many samples and distribution functions are optimized which will give accurate results of the hysteresis loop. The results show the robustness and efficiency of genetic algorithm to model the phenomenon of hysteresis loop. This work can give solutions about the ferromagnetic material evaluations and shows the optimization of distribution functions according to the material behaviors.

scholarly journals Implementation of Preisach-Krasnoselskii Hysteresis Model with the Use of Artificial Neural Networks

2021 ◽  
Author(s):  
Zhao Wang
Keyword(s):  
Neural Networks ◽  
Magnetic Hysteresis ◽  
Feedforward Neural Networks ◽  
Hysteresis Model ◽  
Electromagnetic Devices ◽  
Hysteresis Modeling ◽  
Simulation Results ◽  
And Performance ◽  
Hysteresis Phenomena ◽  
Double Integrals

Accurate modeling of hysteresis is essential for both the design and performance evaluation of electromagnetic devices. This project proposes the use of feedforward meural networks to implement an accurate magnetic hysteresis model based on the mathematical difinition provided by the Preisach-Krasnoselskii (P-K) model. Feedforward neural networks are a linear association networks that relate the ouput patterns to input patterns. By introducing the multi-layer feedforward neural networks make the hysteresis modeling accurate without estimation of double integrals. Simulation results provide the detailed illustrations. The comparisons with the experiments show that the proposed approach is able to satisfactorily reproduce many features of obsereved hysteresis phenomena an in turn can be used for many applications of interest.

Magnetic Hysteresis Modeling

2017 ◽  
Vol 137 (8) ◽  
pp. 530-533
Author(s):  
Tetsuji MATSUO ◽  
Kenji MIYATA
Keyword(s):  
Magnetic Hysteresis ◽  
Hysteresis Modeling
Sign in / Sign up

Export Citation Format

Share Document