Dynamic hysteresis modelling for nano-crystalline cores (original) (raw)
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Prediction of dynamic hysteresis loops of nano-crystalline cores
Expert Systems With Applications, 2009
Dynamic hysteresis loops of a range of nano-crystalline cores have been obtained over a wide frequency range (1-50 kHz). A dynamic hysteresis model from measurements using an artificial neural network trained by the delta-bar-delta learning algorithm has been developed. The input parameters include the geometrical dimensions of cores, peak magnetic induction and magnetizing frequency. The results show the neural network model has an acceptable estimation capability for dynamic hysteresis loops of toroidal nano-crystalline cores.
Neural Network Model for Scalar and Vector Hysteresis
The Preisach model allows to simulate the behaviour of magnetic materials including hysteresis phenomena. It assumes that ferromagnetic materials consist of many elementary interacting domains, and each of them can be represented by a rectangular elementary hysteresis loop. The fundamental concepts of the Preisach model is that different domains have some probability, which can be described by a distribution function, also called the Preisach kernel. On the basis of the so-called Kolmogorov-Arnold theory the feedforward type artificial neural networks are able to approximate any kind of non-linear, continuous functions represented by their discrete set of measurements. A neural network (NN)based scalar hysteresis model has been constructed on the function approximation ability of NNs and if-then type rules about hysteresis phenomena. Vectorial generalization to describe isotropic and anisotropic magnetic materials in two and three dimensions with an original identification method has been introduced in this paper. Good agreement is found between simulated and experimental data and the results are illustrated in figures. K e y w o r d s: hysteresis characteristics, Everett surface, vector hysteresis, feedforward type neural networks, backpropagation training method.
Universal Hysteresis Identification Using Extended Preisach Neural Network
ArXiv, 2020
Hysteresis phenomena have been observed in different branches of physics and engineering sciences. Therefore, several models have been proposed for hysteresis simulation in different fields; however, almost neither of them can be utilized universally. In this paper by inspiring of Preisach Neural Network which was inspired by the Preisach model that basically stemmed from Madelungs rules and using the learning capability of the neural networks, an adaptive universal model for hysteresis is introduced and called Extended Preisach Neural Network Model. It is comprised of input, output and, two hidden layers. The input and output layers contain linear neurons while the first hidden layer incorporates neurons called Deteriorating Stop neurons, which their activation function follows Deteriorating Stop operator. Deteriorating Stop operators can generate non-congruent hysteresis loops. The second hidden layer includes Sigmoidal neurons. Adding the second hidden layer, helps the neural net...
Using neural networks in the identification of Preisach-type hysteresis models
IEEE Transactions on Magnetics, 1998
The identification process of the classical Preisachtype hysteresis model reduces to the determination of the weight function of elementary hysteresis operators upon which the model is built. It is well known that the classical Preisach model can exactly represent hysteretic nonlinearities which exhibit wiping-out and congruency properties. In that case, the model identification can be analytically and systematically accomplished by using first-order reversal curves. If the congruency property is not exactly valid, the Preisach model can only be used as an approximation. It is possible to improve the model accuracy in this situation by incorporating more appropriate experimental data during the identification stage. However, performing this process using the traditional systematic techniques becomes almost impossible. In this paper, the machinery of neural networks is proposed as a tool to accomplish this identification task. The suggested identification approach has been numerically implemented and carried out for a magnetic tape sample that does not possess the congruency property. A comparison between measured data and model predictions suggests that the proposed identification approach yields more accurate results.
A Novel Preisach Based Neural Network Approach to Hysteresis Non-Linearity Modeling
2010
In some systems with hysteresis behavior like Shape Memory Alloy (SMA) actuators and Piezo actuators, we essentially need an accurate modeling of hysteresis either for controller design or performance evaluation. One of the most interesting Hysteresis non-linearity identification methods is Preisach model in which hysteresis is modeled by linear combination of elemental operators. Despite good ability of Preisach modeling to extract main features of system with hysteresis behavior, cause of tough numerical nature of Preisach, it is not convenient to use in real-time control applications. In this paper we present a novel method based on Artificial Neural Network. For evaluation of proposed approach we use experimental apparatus consists of onedimensional flexible aluminum structure with SMA wire as deflection controller actuator which has hysteresis characteristic.
Mathematics
A neural network model to predict the dynamic hysteresis loops and the energy-loss curves (i.e., the energy versus the amplitude of the magnetic induction) of soft ferromagnetic materials at different operating frequencies is proposed herein. Firstly, an innovative Fe-Si magnetic alloy, grade 35H270, is experimentally characterized via an Epstein frame in a wide range of frequencies, from 1 Hz up to 600 Hz. Parts of the dynamic hysteresis loops obtained through the experiments are involved in the training of a feedforward neural network, while the remaining ones are considered to validate the model. The training procedure is accurately designed to, firstly, identify the optimum network architecture (i.e., the number of hidden layers and the number of neurons per layer), and then, to effectively train the network. The model turns out to be capable of reproducing the magnetization processes and predicting the dynamic energy losses of the examined material in the whole range of inducti...
Vector neural network hysteresis model
Physica B: Condensed Matter, 2001
A neural network model of scalar hysteresis phenomena has been developed for modeling the behavior of isotropic magnetic materials. The function approximation ability of artificial neural networks has been applied. The virgin curve and a set of the first-order reversal branches can be stored preliminarily in a system of three neural networks. Different properties of magnetic materials can be simulated by a knowledge-based algorithm. Finally, hysteresis characteristics of different materials predicted by the introduced model are compared with the results of the classical Preisach simulation. Theoretical achievement and results of vector generalization of the method are also introduced.
Hysteresis Modeling and Applications
Advances in Scattering and Biomedical Engineering, 2004
Preisach modeling, long known in the area of magnetics, has introduced mathematical abstraction to the modeling of the highly nonlinear and complex phenomenon of hysteresis. The 2D Preisach-type models presented here, departing slightly from the classical formulation, waive some of its limitations while maintaining the major advantages of simplicity and speed in calculations. Results on different types of ferromagnets are shown, as well as on magnetostrictive materials and shape memory alloys.
Journal of Applied Mathematics, 2011
Preisach model is a well-known hysteresis identification method in which the hysteresis is modeled by linear combination of hysteresis operators. Although Preisach model describes the main features of system with hysteresis behavior, due to its rigorous numerical nature, it is not convenient to use in real-time control applications. Here a novel neural network approach based on the Preisach model is addressed, provides accurate hysteresis nonlinearity modeling in comparison with the classical Preisach model and can be used for many applications such as hysteresis nonlinearity control and identification in SMA and Piezo actuators and performance evaluation in some physical systems such as magnetic materials. To evaluate the proposed approach, an experimental apparatus consisting one-dimensional flexible aluminum beam actuated with an SMA wire is used. It is shown that the proposed ANN-based Preisach model can identify hysteresis nonlinearity more accurately than the classical one. It also has powerful ability to precisely predict the higher-order hysteresis minor loops behavior even though only the first-order reversal data are in use. It is also shown that to get the same precise results in the classical Preisach model, many more data should be used, and this directly increases the experimental cost.