@article{Brunke2001,
   abstract = {Transformer inrush currents are high-magnitude, harmonic-rich currents generated when transformer cores are driven into saturation during energization. These currents have undesirable effects, including potential damage or loss-of-life to the transformer, protective relay misoperation, and reduced power quality on the system. Controlled transformer switching can potentially eliminate these transients if residual core and core flux transients are taken into account in the closing algorithm. This paper explores the theoretical considerations of core flux transients. Based on these studies algorithms were developed which allow controlled energization of most transformers without inrush current. Index Terms-Controlled switching, inrush current, residual flux, transformer switching.},
   author = {John H Brunke and Klaus J Fröhlich and Senior Member},
   issue = {2},
   journal = {IEEE Transactions on Power Delivery},
   title = {Elimination of Transformer Inrush Currents by Controlled Switching-Part I: Theoretical Considerations},
   volume = {16},
   year = {2001},
}

@article{AleaEpis_2009,
title = {Aleatory or epistemic? Does it matter?},
journal = {Structural Safety},
volume = {31},
number = {2},
pages = {105-112},
year = {2009},
doi = {https://doi.org/10.1016/j.strusafe.2008.06.020},
author = {Armen Der Kiureghian and Ove Ditlevsen},
}

@article{Jiles1984,
   author = {D. C. Jiles and D. L. Atherton},
   doi = {10.1063/1.333582},
   issn = {00218979},
   issue = {6},
   journal = {Journal of Applied Physics},
   pages = {2115-2120},
   title = {Theory of ferromagnetic hysteresis (invited)},
   volume = {55},
   year = {1984},
}

@article{Quaranta2020,
   abstract = {This work aims to provide a broad overview of computational techniques belonging to the area of artificial intelligence tailored for identification of nonlinear dynamical systems. Both parametric and nonparametric identification problems are considered. The examined computational intelligence techniques for parametric identification deal with genetic algorithm, particle swarm optimization, and differential evolution. Special attention is paid to the parameters estimation for a rich class of nonlinear dynamical models, including the Bouc–Wen model, chaotic systems, the Jiles–Atherton model, the LuGre model, the Prandtl–Ishlinskii model, the Preisach model, and the Wiener–Hammerstein model. On the other hand, genetic programming and artificial neural networks are discussed for nonparametric identification applications. Once the identification problem is formulated, a detailed illustration of the considered computational intelligence techniques is provided, together with a comprehensive examination of relevant applications in the fields of structural mechanics and engineering. Possible directions for future research are also addressed.},
   author = {Giuseppe Quaranta and Walter Lacarbonara and Sami F. Masri},
   doi = {10.1007/s11071-019-05430-7},
   issn = {1573269X},
   issue = {2},
   journal = {Nonlinear Dynamics},
   keywords = {Artificial neural network,Computational intelligence,Differential evolution,Genetic algorithm,Genetic programming,Nonlinear system identification,Particle swarm optimization},
   pages = {1709-1761},
   publisher = {Springer},
   title = {A review on computational intelligence for identification of nonlinear dynamical systems},
   volume = {99},
   year = {2020},
}

@article{Leite2004,
   abstract = {The parameters set of the Jiles-Atherton hysteresis model is identified by using a real coded genetic algorithm. The parameters identification is performed by minimizing the mean squared error between experimental and simulated magnetic field curves. The procedure is validated by comparing experimental and simulated results.},
   author = {J. V. Leite and S. L. Avila and N. J. Batistela and W. P. Carpes and N. Sadowski and P. Kuo-Peng and J. P.A. Bastos},
   doi = {10.1109/TMAG.2004.825319},
   issn = {00189464},
   issue = {2 II},
   journal = {IEEE Transactions on Magnetics},
   keywords = {Magnetic hysteresis modeling,Magnetic materials,Parameters identification},
   pages = {888-891},
   publisher = {Institute of Electrical and Electronics Engineers Inc.},
   title = {Real coded genetic algorithm for Jiles-Atherton model parameters identification},
   volume = {40},
   year = {2004},
}

@article{Li2020,
   author = {Qian Li and San Yang Liu and Xin She Yang},
   doi = {10.1016/j.asoc.2020.106193},
   issn = {15684946},
   journal = {Applied Soft Computing Journal},
   keywords = {Cuckoo search,Differential evolution,Initialization,Particle swarm optimization,Probability distribution},
   publisher = {Elsevier Ltd},
   title = {Influence of initialization on the performance of metaheuristic optimizers},
   volume = {91},
   year = {2020},
}

@article{Ugarte24,
   abstract = {Transformers are one of the key assets in AC distribution grids and renewable power integration. During transformer energization inrush currents appear, which lead to transformer degradation and can cause grid instability events. These inrush currents are a consequence of the transformer's magnetic core saturation during its connection to the grid. Transformer cores are normally modelled by the Jiles-Atherton (JA) model which contains five parameters. These parameters can be estimated by metaheuristic-based search algorithms. The parameter initialization of these algorithms plays an important role in the algorithm convergence. The most popular strategy used for JA parameter initialization is a random uniform distribution. However, techniques such as parameter initialization by Probability Density Functions (PDFs) have shown to improve accuracy over random methods. In this context, this research work presents a framework to assess the impact of different parameter initialization strategies on the performance of the JA parameter estimation for inrush current studies. Depending on available data and expert knowledge, uncertainty levels are modelled with different PDFs. Moreover, three different metaheuristic-search algorithms are employed on two different core materials and their accuracy and computational time are compared. Results show an improvement in the accuracy and computational time of the metaheuristic-based algorithms when PDF parameter initialization is used.},
   author = {Jone Ugarte and Jose I. Aizpurua and Manex Barrenetxea },
   doi = {10.1109/TPWRD.2024.3398790},
   issn = {19374208},
   journal = {IEEE Transactions on Power Delivery},
   keywords = {Inrush current,jiles-Atherton model,metaheuristic-based search,probability density function,transformer,uncertainty},
   publisher = {Institute of Electrical and Electronics Engineers Inc.},
   title = {Uncertainty Distribution Assessment of Jiles-Atherton Parameter Estimation for Inrush Current Studies},
   volume = {39},
   issue = {4},
   pages = {2275-2285},
   year = {2024}
}

@article{Karanki2009,
   abstract = {A wide range of uncertainties will be introduced inevitably during the process of performing a safety assessment of engineering systems. The impact of all these uncertainties must be addressed if the analysis is to serve as a tool in the decision-making process. Uncertainties present in the components (input parameters of model or basic events) of model output are propagated to quantify its impact in the final results. There are several methods available in the literature, namely, method of moments, discrete probability analysis, Monte Carlo simulation, fuzzy arithmetic, and Dempster-Shafer theory. All the methods are different in terms of characterizing at the component level and also in propagating to the system level. All these methods have different desirable and undesirable features, making them more or less useful in different situations. In the probabilistic framework, which is most widely used, probability distribution is used to characterize uncertainty. However, in situations in which one cannot specify (1) parameter values for input distributions, (2) precise probability distributions (shape), and (3) dependencies between input parameters, these methods have limitations and are found to be not effective. In order to address some of these limitations, the article presents uncertainty analysis in the context of level-1 probabilistic safety assessment (PSA) based on a probability bounds (PB) approach. PB analysis combines probability theory and interval arithmetic to produce probability boxes (p-boxes), structures that allow the comprehensive propagation through calculation in a rigorous way. A practical case study is also carried out with the developed code based on the PB approach and compared with the two-phase Monte Carlo simulation results. © 2009 Society for Risk Analysis.},
   author = {Durga Rao Karanki and Hari Shankar Kushwaha and Ajit Kumar Verma and Srividya Ajit},
   doi = {10.1111/j.1539-6924.2009.01221.x},
   issn = {02724332},
   issue = {5},
   journal = {Risk Analysis},
   keywords = {Epistemic and aleatory uncertainty,Monte Carlo simulation,Probabilistic safety assessment,Probability bounds,Unavailability},
   pages = {662-675},
   pmid = {19302279},
   title = {Uncertainty analysis based on probability bounds (P-Box) approach in probabilistic safety assessment},
   volume = {29},
   year = {2009},
}

@phdthesis{Chiesa2010,
    author = {Nicola Chiesa},
    title = {Power Transformer Modeling for Inrush Current Calculation},
    school = {Norwegian University of Science and Technology},
    year = {2010}
}

@book{Jiles1991,
   abstract = {This upper-division textbook on solid state physics describes how the excitations and imperfections of actual solids can be understood with simple models. Recent advances in the field have been added including high temperature superconductors, quasicrystals, nanostructures, superlattices, Bloch/Wannier levels, Zener tunneling, light-emitting diodes and new magnetic materials.},
   author = {David Jiles},
   doi = {10.1007/978-1-4615-3868-4},
   journal = {Introduction to Magnetism and Magnetic Materials},
   publisher = {Springer US},
   title = {Introduction to Magnetism and Magnetic Materials},
   year = {1991},
}

@article{Mork2007,
   abstract = {A new topologically correct hybrid transformer model is developed for low and midfrequency transient simulations. Power transformers have a conceptually simple design, but behaviors can be very complex. The selection of the most suitable representation for a given behavior depends on the type of transformer to be simulated, the frequency range, and other factors, such as the internal design of the transformer and available parameters or design data. Here, a modular model suitable for frequencies up to 3-5 kHz is developed, utilizing a duality-based lumped-parameter saturable core, matrix descriptions of leakage and capacitive effects, and frequency-dependent coil resistance. Implementation and testing of this model was done for 15-kVA 208Δ-120Y 3-legged and 150-kVA 12,470Y-208Y 5-legged transformers. The basis and development of the model is presented, along with a discussion of necessary parameters and the approaches for obtaining them. © 2006 IEEE.},
   author = {Bruce A. Mork and Francisco Gonzalez and Dmitry Ishchenko and Don L. Stuehm and Joydeep Mitra},
   doi = {10.1109/TPWRD.2006.883000},
   issn = {08858977},
   issue = {1},
   journal = {IEEE Transactions on Power Delivery},
   keywords = {Duality,Electromagnetic Transients Program (EMTP),Transformer models,Transient simulations},
   pages = {248-255},
   title = {Hybrid transformer model for transient simulation - Part I: Development and parameters},
   volume = {22},
   year = {2007},
}

@article{Annakkage2000,
   abstract = {The hysteresis loop of the core material in a test CT is simulated using the Jiles-Atherton theory. Comparisons are made between recorded and simulated waveforms and it is found necessary to replace the modified Langevin function used by Jiles and Atherton. Using an alternative function described in the paper, good agreement is achieved between test and simulated waveforms.},
   author = {U D Annakkage and P G Mclaren and E Dirks and R P Jayasinghe and A D Parker},
   issue = {1},
   journal = {IEEE TRANSACTIONS ON POWER DELIVERY},
   keywords = {Index Terms-loop,current transformers,hysteresis,sim-ulation},
   pages = {57},
   title = {A Current Transformer Model Based on the Jiles-Atherton Theory of Ferromagnetic Hysteresis},
   volume = {15},
   year = {2000},
}

@book{Sadowski2003,
  title={Electromagnetic Modelling by Finite Element Methods},
  author={Bastos, Jo{\~a}o Pedro A and Sadowski, Nelson},
  year={2003},
  pages={438-442},
  publisher={CRC press}
}

@article{Sarker2022,
   abstract = {The physical behaviour of a magnetic material can be characterized by Jiles-Atherton (J-A) model where some model parameters are generally identified by optimization techniques. For identification of model parameters using optimization techniques, an error criterion based on the error between the measured and calculated magnetic flux density (B) or magnetic field strength (H) is commonly considered where the relative error in the calculation of iron loss is ignored. Consequently, the calculated iron loss from B-H loop sometimes highly differs from its experimental value. In this paper, the error criteria for J-A model's parameter identification are designed as the combination of the relative iron loss error criterion and the general existing error criterion. Furthermore, a modified J-A model is proposed to improve the agreement between experimental and calculated results especially at the low magnetic induction levels by introducing a scaling factor in the anhysteretic magnetization. The proposed modified J-A model and the effectiveness of the error criteria for its parameter identification are tested by comparing calculated results with the experimental results as well as recently works in the literature.},
   author = {Pejush Chandra Sarker and Youguang Guo and Haiyan Lu and Jian Guo Zhu},
   doi = {10.1016/j.jmmm.2021.168602},
   issn = {03048853},
   journal = {Journal of Magnetism and Magnetic Materials},
   keywords = {Anhysteretic magnetization,Fe-based amorphous magnetic material,Hysteresis and dynamic losses,Jiles-Atherton model},
   publisher = {Elsevier B.V.},
   title = {Improvement on parameter identification of modified Jiles-Atherton model for iron loss calculation},
   volume = {542},
   year = {2022},
}

@article{Chwastek2006,
   abstract = {The paper is concerned about the application of genetic algorithms to the estimation of magnetic hysteresis model parameters, based on the description of magnetization process proposed by Jiles and Atherton. The genetic algorithm approach has been applied to estimation of model parameters for two magnetic materials - a conventional non-oriented steel and a modern amorphous material. The errors between the modelled and experimental data points on M - H plane have been determined. The results have been compared with the results obtained with the previously proposed approach, based on direct search method. It has been proved that the genetic algorithm approach results in lower percentage errors. © 2006 IMACS.},
   author = {Krzysztof Chwastek and Jan Szczyglowski},
   doi = {10.1016/j.matcom.2006.01.002},
   issn = {03784754},
   issue = {3},
   journal = {Mathematics and Computers in Simulation},
   keywords = {Estimation,Genetic algorithms,Magnetic hysteresis model,Model parameters},
   pages = {206-211},
   title = {Identification of a hysteresis model parameters with genetic algorithms},
   volume = {71},
   year = {2006},
}

@article{Coelho2012,
   abstract = {The term swarm intelligence is used to describe algorithms and distributed problem solvers inspired by the collective behavior of insect colonies and other animal societies. Particle swarm optimization (PSO) is a kind of swarm intelligence that is based on the social behavior metaphor. Furthermore, PSO is a stochastic search technique with reduced memory requirement, computationally effective and easier to implement compared to other optimization metaheuristics. Unlike the traditional optimization algorithms, PSO is a derivative-free algorithm and thus it is especially effective in dealing with complex and nonlinear problems in electromagnetic optimization applications. In this paper, a multiobjective PSO approach based on exponential distribution probability operator (MOPSO-E) is proposed and evaluated. Numerical comparisons with results using a multiobjective PSO with external archiving and the proposed MOPSO-E demonstrated that the performance of the MOPSO-E is promising in Jiles-Atherton vector hysteresis model parameter identification. The proposed MOPSO-E to find nondominated solutions that represent the good trade-offs among the objectives in the evaluated case study. © 2012 IEEE.},
   author = {Leandro Dos S. Coelho and Fábio A. Guerra and Jean V. Leite},
   doi = {10.1109/TMAG.2011.2172581},
   issn = {00189464},
   issue = {2},
   journal = {IEEE Transactions on Magnetics},
   keywords = {Electromagnetics,evolutionary computation,optimization,swarm intelligence},
   pages = {283-286},
   publisher = {Institute of Electrical and Electronics Engineers Inc.},
   title = {Multiobjective exponential particle swarm optimization approach applied to hysteresis parameters estimation},
   volume = {48},
   year = {2012},
}

@article{Dezelak2014,
   abstract = {The proposed paper deals with a nonlinear dynamic model of a welding transformer, where the effects of hysteresis losses are accounted for by the simplified method and by the inverse form of the Jiles-Atherton hysteresis model. Both models have been modified in such a way that they can be used when the magnetically nonlinear characteristic of the entire device is determined experimentally. The aforementioned characteristics are given in the form of approximation polynomials. For defining magnetically nonlinear characteristic and both the Jiles-Atherton hysteresis model and the simplified model, an optimization procedure based on the differential evolution is applied for obtaining the parameters of applied approximation polynomials. The main goal of this paper is to evaluate both discussed models regarding their suitability to be included into dynamic model of a welding transformer used in the optimization of the entire resistance spot welding system. The evaluation focuses on achieved accuracy and computational effort required to implement the discussed models.},
   author = {Klemen Deželak and Martin Petrun and Beno Klopčič and Drago Dolinar and Gorazd Štumberger},
   doi = {10.1109/TMAG.2013.2285736},
   issn = {00189464},
   issue = {4},
   journal = {IEEE Transactions on Magnetics},
   keywords = {Hysteresis loop,Jiles-Atherton model,optimization methods,transformers},
   publisher = {Institute of Electrical and Electronics Engineers Inc.},
   title = {Usage of a Simplified and Jiles-Atherton Model When Accounting for the Hysteresis Losses Within a Welding Transformer},
   volume = {50},
   year = {2014},
}

@article{Knypinski2012,
   abstract = {In the paper an algorithm and computer code for the identification of the hysteresis parameters of the Jiles-Atherton model have been presented. For the identification the particle swarm optimization method (PSO) has been applied. In the optimization procedure five design variables has been assumed. The computer code has been elaborated using Delphi environment. Three types of material have been examined. The results of optimization have been compared to experimental ones. Selected results of the calculation for different material are presented and discussed.},
   author = {Åukasz Knypiński and Lech Nowak and Piotr Sujka and Kazimierz Radziuk},
   doi = {10.2478/v10171-012-0013-3},
   issn = {00040746},
   issue = {2},
   journal = {Archives of Electrical Engineering},
   keywords = {Jiles-Atherton model,hysteresis,optimization,particle swarm optimization method},
   pages = {139-148},
   title = {Application of a PSO algorithm for identification of the parameters of Jiles-Atherton hysteresis model},
   volume = {61},
   year = {2012},
}

@article{Toman2008,
   author = {Matej Toman and Gorazd Stumberger and Drago Dolinar},
   issue = {6},
   journal = {IEEE Transaction on Magnetics},
   pages = {1098-1101},
   title = {Parameter Identification of the Jiles–Atherton Hysteresis Model Using Differential Evolution},
   volume = {44},
   year = {2008},
}

@article{Jiang2018,
   abstract = {Traditional structural uncertainty analysis is mainly based on probability models and requires the establishment of accurate parametric probability distribution functions using large numbers of experimental samples. In many actual engineering problems, the probability distributions of some parameters can be established due to sufficient samples available, whereas for some parameters, due to the lack or poor quality of samples, only their variation intervals can be obtained, or their probability distribution types can be determined based on the existing data while some of the distribution parameters such as mean and standard deviation can only be given interval estimations. This thus will constitute an important type of probability-interval hybrid uncertain problem, in which the aleatory and epistemic uncertainties both exist. The probability-interval hybrid uncertainty analysis provides an important mean for reliability analysis and design of many complex structures, and has become one of the research focuses in the field of structural uncertainty analysis over the past decades. This paper reviews the four main research directions in this area, i.e., uncertainty modeling, uncertainty propagation analysis, structural reliability analysis, and reliability-based design optimization. It summarizes the main scientific problems, technical difficulties, and current research status of each direction. Based on the review, this paper also provides an outlook for future research in probability-interval hybrid uncertainty analysis.},
   author = {C. Jiang and J. Zheng and X. Han},
   doi = {10.1007/s00158-017-1864-4},
   issn = {16151488},
   issue = {6},
   journal = {Structural and Multidisciplinary Optimization},
   keywords = {Aleatory uncertainty,Epistemic uncertainty,Probability-interval hybrid uncertainty,Reliability analysis,Reliability-based design optimization,Uncertainty propagation analysis},
   pages = {2485-2502},
   publisher = {Springer Verlag},
   title = {Probability-interval hybrid uncertainty analysis for structures with both aleatory and epistemic uncertainties: a review},
   volume = {57},
   year = {2018},
}

@article{Aizpurua2019,
   abstract = {The remaining useful life (RUL) of transformer insulation paper is largely determined by the winding hot-spot temperature (HST). Frequently the HST is not directly monitored and it is inferred from other measurements. However, measurement errors affect prediction models and if uncertain variables are not taken into account this can lead to incorrect maintenance decisions. Additionally, existing analytic models for HST calculation are not always accurate because they cannot generalize the properties of transformers operating in different contexts. In this context, this paper presents a novel transformer condition assessment approach integrating uncertainty modeling, data-driven forecasting models, and model-based experimental models to increase the prediction accuracy and handle uncertainty. The proposed approach quantifies the effect of measurement errors on transformer RUL predictions and confirms that temperature and load measurement errors affect the RUL estimation. Forecasting results show that the extreme gradient boosting (XGB) algorithm best captures the nonlinearities of the thermal model and improves the prediction accuracy among a number of forecasting approaches. Accordingly, the XGB model is integrated with experimental models in a particle filtering framework to improve thermal modeling and RUL prediction tasks. Models are tested and validated using a real dataset from a power transformer operating in a nuclear power plant.},
   author = {Jose Ignacio Aizpurua and Stephen D.J. McArthur and Brian G. Stewart and Brandon Lambert and James G. Cross and Victoria M. Catterson},
   doi = {10.1109/TIE.2018.2860532},
   issn = {02780046},
   issue = {6},
   journal = {IEEE Transactions on Industrial Electronics},
   keywords = {Condition assessment,forecasting,prognostics and health management (PHM),sensitivity,transformers},
   pages = {4726-4737},
   publisher = {Institute of Electrical and Electronics Engineers Inc.},
   title = {Adaptive Power Transformer Lifetime Predictions Through Machine Learning and Uncertainty Modeling in Nuclear Power Plants},
   volume = {66},
   year = {2019},
}

@data{Bhadra2022,
doi = {10.21227/my4x-mz70},
url = {https://dx.doi.org/10.21227/my4x-mz70},
author = {Bhadra, Ananta and Jalilzadeh Hamidi, Reza},
publisher = {IEEE Dataport},
title = {A Guidebook on Jiles-Atherton Model Parameter Approximation Based on Simulated B-H Curves},
year = {2022} }

@article{Rubežić2018,
   abstract = {Purpose: The purpose of this paper is to propose a chaotic optimization method for identifying the parameters of the Jiles–Atherton (J-A) hysteresis model. Design/methodology/approach: The J-A model has five parameters which are assigned with physical meaning and whose determination is demanding. To determine these parameters, the fitness function, which represents the difference between the measured and the modeled hysteresis loop, is formed. Optimal parameter values are the values that minimize the fitness function. Findings: The parameters of J-A model for three magnetic materials are determined. The model with the optimal parameters is validated using measured data and comparison with particle swarm optimization algorithm, genetic algorithm, pattern search and simulated annealing algorithm. The results show that the proposed method provides better agreement between measured and modeled hysteresis loop than other methods used for comparison. The proposed method is also suitable for simultaneous optimization of multiple hysteresis loops. Originality/value: Chaotic optimization method is implemented for the first time for J-A model parameter identification. Numerical comparisons with results obtained with other optimization algorithms demonstrate that this method is a suitable alternative in parameters identification of J-A hysteresis model. Furthermore, this method is easy to implement and set up.},
   author = {Vesna Rubežić and Luka Lazović and Ana Jovanović},
   doi = {10.1108/COMPEL-11-2017-0496},
   issn = {03321649},
   issue = {6},
   journal = {COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering},
   keywords = {Chaotic optimization,Hysteresis,Jiles–Atherton Model,Magnetic material modelling},
   pages = {2067-2080},
   publisher = {Emerald Group Holdings Ltd.},
   title = {Parameter identification of Jiles–Atherton model using the chaotic optimization method},
   volume = {37},
   year = {2018},
}


@article{Naghizadeh2012,
   abstract = {Purpose - The purpose of this paper is to implement a simple, fast and accurate heuristic method for parameter determination of Jiles-Atherton (JA) hysteresis model for representing magnetization in electrical steel sheets. The performance of the method is validated using measured data and comparison with previous methods. Design/methodology/approach - JA model requires five parameters to represent the hysteretic behavior of ferromagnetic materials. In order to determine these parameters, measured hysteresis loop is used here to calculate a fitness function which is defined by comparing the measured and simulated magnetization loops. This fitness function is minimized by optimization algorithms. Findings - In total, four different measured hysteresis loops are studied in this paper. Each optimization algorithm is executed 50 times to investigate the convergence, speed, and accuracy of six methods. All methods begin with the same randomly generated initial parameters. Physical boundaries are used for parameters to avoid unaccepted results. Thorough examination of results shows that the proposed method is more appropriate than previously implemented methods for the parameter determination of Jiles-Atherton model in all studied cases. The required parameters for each optimization method are also presented. Originality/value - Shuffled frog leaping algorithm (SFLA) is implemented for the first time for JA model parameter determination. The results show that SFLA is faster and more accurate in comparison with other methods. Furthermore, this algorithm is easy to implement and tune. © 2012 Emerald Group Publishing Limited.},
   author = {Ramezan Ali Naghizadeh and Behrooz Vahidi and Seyed Hossein Hosseinian},
   doi = {10.1108/03321641211227573},
   issn = {03321649},
   issue = {4},
   journal = {COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering},
   keywords = {Hysteresis,Jiles-Atherton model,Magnetization,Optimization,Sheet metal,Steel},
   pages = {1293-1309},
   title = {Parameter identification of Jiles-Atherton model using SFLA},
   volume = {31},
   year = {2012},
}


