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The purpose of this paper is to develop a viscous-type frequency dependent scalar Preisach hysteresis model and to identify the model using measured data and nonlinear numerical field analysis. The hysteresis model must be fast and well applicable in electromagnetic field simulations.

Iron parts of electrical machines are made of non-oriented isotropic ferromagnetic materials. The finite element method (FEM) is usually applied in the numerical field analysis and design of this equipment. The scalar Preisach hysteresis model has been implemented for the simulation of static and dynamic magnetic effects inside the ferromagnetic parts of different electrical equipment.

The comparison between measured and simulated data using a toroidal core shows a good agreement. A modified nonlinear version of TEAM Problem No. 30.a is also shown to test the hysteresis model in the FEM procedure.

The dynamic model is an extension of the static one; an extra magnetic field intensity term is added to the output of the static inverse model. This is a viscosity-type dynamic model. The fixed-point method with stable scheme has been realized to take frequency dependent anomalous losses into account in FEM. This scheme can be used efficiently in the frame of any potential formulations of Maxwell's equations.

The purpose of this paper is to present a procedure, which determines the magnetic force acting between a soft magnetic cylinder and a coil taking the hysteresis phenomena into account.

The magnetic force is computed replacing the ferromagnetic body with an equivalent magnetic moment distribution. Isotropic vector Preisach model with analytical expressed Everett function describes the magnetic properties of the ferromagnetic material. The magnetization distribution is calculated applying the integral equation method. The Preisach hysteresis model is included in the iteration process based on Picard‐Banach scheme.

In the case of integral equation method the unknown quantities are the magnetization and the magnetic field intensity. In this way the Preisach hysteresis model can be included in a convenient way in the iteration procedure. Knowing the magnetization distribution the magnetic force can be determined. The developed algorithms can be applied in tubular linear motor design.

The paper presents a new formulation of the Preisach hysteresis model. With the aim of the analytically expressed Everett function a stable and faster algorithm can be realized to determine the magnetic force in arrangements with ferromagnetic parts.

The classical Preisach model and its modifications are one of the most generally applied simulations to model the behavior of magnetic materials, to describe hysteresis phenomena and different properties, as noncongruent minor loops, frequency dependence, temperature dependence, accommodation, and so on. Artificial neural networks (NNs) are widely used in fields of research where the solution of problems with conventional methods on traditional computers is very difficult to work out, for example system identification, modeling and function approximation. NNs can be considered as universal approximation for functions based on the theorem of Kolmogorov‐Arnold. In this paper a new NN model of scalar hysteresis characteristics is introduced. The examined method is built on the function approximation and continuous interpolation capability of NNs. The anhysteretic magnetization curve and a set of the ascending and a set of the descending first order reversal branches can be stored in a system of three neural networks. Different properties of magnetic materials can be simulated by a simple knowledge‐based algorithm. Value of differential susceptibility can be expressed in analytical form. Finally hysteresis characteristics predicted by the introduced model are compared with the results of the Preisach simulation.

The purpose of this paper is to present a Preisach model to simulate the vector hysteresis properties of ferromagnetic materials.

The vector behavior has been studied at low frequency applying a single‐sheet tester with a round‐shaped specimen, and the locus of the magnetic flux density vector has been controlled by a digital measurement system. An inverse vector Preisach hysteresis model has been developed and identified by using the measured data.

Finally, the inverse model has been inserted into a finite element procedure through the combination of the fixed point technique and the reduced magnetic scalar potential formulation. The developed single‐sheet tester measurement system has been simulated. The applicability of the realized measurement system as well as the developed model has been proven by comparing measured and simulated results.

The identification technique is original, based on a previous work of the author.

Widely used in micro‐position devices and vibration control, the piezoelectric actuator exhibits strong hysteresis effects, which can cause inaccuracy and oscillations, even lead to instability. If the hysteretic effects can be predicted, a controller can be designed to correct for these effects. This paper aims to present a neural network hysteresis model with an improved Preisach model to predict the output of piezoelectric actuator.

The improved Preisach model is given: A wiping‐out memory sequence is defined that is along a single axis only and at the same time the ascending and the descending extreme points are separated. The extended area variable is calculated according to wiping‐out memory sequence. The relationship between the two inputs (the extended area variable and variable rate of input signal) and the hysteresis output is implemented with a neural network to approximate the hysteresis model for the piezoelectric actuators.

Some experiments are carried out with a piezoelectric ceramic (PST150/7/40 VS12) and the results show the neural network hysteresis model can reliably predict the hysteretic behaviours in piezoelectric actuators.

The improved Preisach model is a simple model that is implemented by a neural network to reliably predict the hysteretic output in piezoelectric actuators.

On the basis of the Kolmogorov‐Arnold theory, the feedforward type artificial neural networks (NNs) are able to approximate any kind of nonlinear, continuous functions represented by its discrete set of measurements. A NN‐based scalar hysteresis model has been constructed preliminarily on the function approximation ability of NNs. An if‐then type knowledge‐base represents the properties of the hysteresis characteristics. 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.

The purpose of this paper is to study the optimization of a pulsed-excitation gradiometric inductive sensing system.

The authors applied numerical finite-element modeling for the simulation of the step responses of different target materials to identify the particular contribution of the magnetic permeability and the electric conductivity. Four materials of technical importance (aluminum, copper, constructional steel and stainless steel) and four fictive test materials were modeled for the comparison of different materials possessing a wide range of combinations of material parameters. A microcontroller-based measurement setup was implemented for the qualitative validation of the simulation results. A simple signal processing chain was also applied for the time-domain conversion of the direct step response signals to increase the time scale of the signals to be processed by common mixed-signal components.

The step response signals contain relevant information of the target material quality and the sensor-to-target distance. The target materials can be distinguished and the sensor-target distance can be determined by the evaluation of the step response signals with an appropriate algorithm based on the measurement of the time and voltage of an extreme of the time dependent measurement signals. Both direct and time-domain converted signals can be used for material independent proximity sensing.

In order to design an inductive proximity switch, an evaluation method of the response signals has been proposed by using an analog RLC circuit. With the presented method, a target material invariant inductive proximity switch can be realized.

This paper deals with the determination of the electric field for the electric precipitators. The numerical results are determined by the help of the R‐functions method. The results are plotted in figures and compared with measured ones.

In this paper the kinetic behavior of a non‐magnetic cube, plated on two opposite sides with ferromagnetic coating, situated on a horizontal plane surface and immersed in a homogeneous magnetic field is investigated. The created magnetic torque is determined, the involved field quantities are computed applying the integral equation method taking into account the hysteresis of the ferromagnetic coating by a non‐linear iterative procedure based on the Piccard‐Banach fixed point technique. Considering the friction between the piece and the plane surface the equation of motion is solved. The magnetic field strength necessary to rotate the piece in a required direction is determined.

The increase of the accuracy of a mathematical model of hysteresis by the choice of the optimum saturation curve for a given material.

Hysteresis loops of typical soft magnetic materials are approximated with the help of the Taka´cs magnetization model using different saturation curves. The quality of approximations is determined by the deviation of computed magnetic induction amplitudes, iron losses, apparent remanences and coercivities from the measured values.

By the proper choice of saturation curve, the relative inaccuracy of approximations can be reduced with reference to the original model based on tangent hyperbolic function.

The accuracy of approximations worsens close to saturation because of the excessive rise of magnetization due to the linear term of the model. This effect should be minimized by the application of complex saturation curves using greater number of parameters.

Owing to the convenient analytical form and increased accuracy, the model equations can be used in simpler practical evaluations of hysteresis effects and for teaching purposes.

Presented form of model equations enables approximation of hysteresis loops and the evaluation of main characteristics of magnetic materials on the basis of any saturation curve.