In this paper a procedure for the identification of the parameters of the Jiles-Atherton (JA) model is presented. The parameters of the JA model of a material are found by using a neural network trained by a collection of hysteresis curves, whose parameters are known. After a presentation of the Jiles-Atherton model, the neural network and the training procedure are described and the method is validated by using some numerical, as well as experimental, data
A neural network model to predict the dynamic hysteresis loops and the energy-loss curves (i.e., the...
Power converters often features inductive devices in their architectures. Accurate simulation of the...
The focus of this work is on the development and utilization of artificial neural networks (ANNs) fo...
In this paper a procedure for the identification of the parameters of the Jiles-Atherton (JA) model ...
International audienceIn this work we have presented an approach for calculating the hysteresis loop...
In this paper, parameters of the Jiles-Atherton (J-A) hysteresis model are identified using a stocha...
This paper presents a methodology for identifying reduced vector Preisach model parameters by using ...
A computationally efficient and robust neural network-based model to reproduce the hysteresis phenom...
This paper presents a methodology for identifying Reduced Vector Preisach Model parameters by using...
This paper presents a algorithm to fit the major hysteresis loop and first magnetization curve. The ...
In this paper Jiles Atherton (JA) Model is used to obtain a mathematical model of the hysteresis in...
The modelling of magnetic components of electromagnetic devices requires an accurate representation ...
In this work, a Preisach-recurrent neural network model is proposed to predict the dynamic hysteresi...
This paper presents a Jiles–Atherton hysteresis model identification method based on a partnership o...
The present investigation aims at the definition of an efficient and robust neural network-based mod...
A neural network model to predict the dynamic hysteresis loops and the energy-loss curves (i.e., the...
Power converters often features inductive devices in their architectures. Accurate simulation of the...
The focus of this work is on the development and utilization of artificial neural networks (ANNs) fo...
In this paper a procedure for the identification of the parameters of the Jiles-Atherton (JA) model ...
International audienceIn this work we have presented an approach for calculating the hysteresis loop...
In this paper, parameters of the Jiles-Atherton (J-A) hysteresis model are identified using a stocha...
This paper presents a methodology for identifying reduced vector Preisach model parameters by using ...
A computationally efficient and robust neural network-based model to reproduce the hysteresis phenom...
This paper presents a methodology for identifying Reduced Vector Preisach Model parameters by using...
This paper presents a algorithm to fit the major hysteresis loop and first magnetization curve. The ...
In this paper Jiles Atherton (JA) Model is used to obtain a mathematical model of the hysteresis in...
The modelling of magnetic components of electromagnetic devices requires an accurate representation ...
In this work, a Preisach-recurrent neural network model is proposed to predict the dynamic hysteresi...
This paper presents a Jiles–Atherton hysteresis model identification method based on a partnership o...
The present investigation aims at the definition of an efficient and robust neural network-based mod...
A neural network model to predict the dynamic hysteresis loops and the energy-loss curves (i.e., the...
Power converters often features inductive devices in their architectures. Accurate simulation of the...
The focus of this work is on the development and utilization of artificial neural networks (ANNs) fo...