Advanced SearchSearch Tips
Dynamic Hysteresis Model Based on Fuzzy Clustering Approach
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Dynamic Hysteresis Model Based on Fuzzy Clustering Approach
Mourad, Mordjaoui; Bouzid, Boudjema;
  PDF(new window)
Hysteretic behavior model of soft magnetic material usually used in electrical machines and electronic devices is necessary for numerical solution of Maxwell equation. In this study, a new dynamic hysteresis model is presented, based on the nonlinear dynamic system identification from measured data capabilities of fuzzy clustering algorithm. The developed model is based on a Gustafson-Kessel (GK) fuzzy approach used on a normalized gathered data from measured dynamic cycles on a C core transformer made of 0.33mm laminations of cold rolled SiFe. The number of fuzzy rules is optimized by some cluster validity measures like `partition coefficient` and `classification entropy`. The clustering results from the GK approach show that it is not only very accurate but also provides its effectiveness and potential for dynamic magnetic hysteresis modeling.
Cluster validity;Dynamic magnetic hysteresis;Gustafson-kessel algorithm;Model identification;
 Cited by
Application of Joint Electro-Chemical Detection for Gas Insulated Switchgear Fault Diagnosis,;;;

Journal of Electrical Engineering and Technology, 2015. vol.10. 4, pp.1765-1772 crossref(new window)
Hybrid control of magnetically controlled shape memory alloy actuator based on Krasnosel’skii-Pokrovskii model, Journal of Intelligent & Fuzzy Systems, 2015, 29, 1, 63  crossref(new windwow)
Application of Joint Electro-Chemical Detection for Gas Insulated Switchgear Fault Diagnosis, Journal of Electrical Engineering and Technology, 2015, 10, 4, 1765  crossref(new windwow)
D. C. Jiles and D. L. Atherton, "Theory of ferromagnetic hysteresis," J.Magn. Magn. Mater. Vol. 61, pp. 48-60, 1986. crossref(new window)

F. Preisach, "Uber die magnetische nachwirkung" (in German), Zeitschrift für physik, Vol. 94, No. 5-6, p. 277, 1935. crossref(new window)

M.Brokate and J. Sprekels, Hysteresis and phase transitions. New York: Springer-Verlag, 1996.

I. D. Mayergoyz, Mathematical Models of hysteresis. New York: Springer-Verlag, 1991.

M. L. Hodgdon, "Mathematical Theory and Calculations of Magnetic Hysteresis Curves", IEEE Trans. on Magnetics, Vol. 24, pp. 3120-3122, 1988. crossref(new window)

Siamak Tafazoli, Mathieu Leduc and Xuehong Sun "Hysteresis Modeling using Fuzzy Subtractive Clustering", International journal of computational cognition, Vol. 4, No. 3, pp 15-27, Sep 2006.

Francisco Riganti Fulginei and Alessandro Salvini, "Soft computing for the identification of the Jiles- Atherton Model Parameters", IEEE Transactions on Magnetics, Vol. 41, No. 3, pp. 1100-1108, March 2005. crossref(new window)

M. Mordjaoui, M. Chabane and B. Boudjema "Dynamic magnetic hysteresis modeling with an adaptive Neuro-Fuzzy Inference System" International Journal of computational cognition, Vol. 5 $N^{\circ}$. 2 pp 25-30. Juin 2007.

M. Mordjaoui, M. Chabane and B. Boudjema, "Hysteresis modeling with adaptive neuro-Fuzzy inference system", Journal of Ferroelectrics, Francis and Taylor. Vol. 372, $N^{\circ}$1 pp. 54-65 Nov 2008. crossref(new window)

A. Salvini, F.R. Fulginei, and C. Coltelli, "A Neuro- Genetic and Time-Frequency Approach to Macromodeling Dynamic Hysteresis in the Harmonic Regime," IEEE Transactions on Magnetics, Vol. 39, No. 3, pp. 1401-1404, May 2003. crossref(new window)

F. R. Fulginei and A. Salvini, "Neural network approach for modeling magnetic materials under distorted excitation"s, IEEE Transactions on Magnetics, Vol. 48, No. 2, pp. 307-310, February 2012. crossref(new window)

M. Saghafifar, A. Kundu and A. Nafalski, "Dynamic magnetic hysteresis modeling using Elman recurrent neural network", International Journal of Applied Electromagnetics and Mechanics, IOS Press, Amsterdam, Vol. 13, pp. 209-214, 2002.

Bezdek, J.C. and Dunn, J.C, "Optimal fuzzy partition: A heuristic for estimating the parameters in a mixture of normal distributions". IEEE Trans. Comput., Vol. C-24, pp. 835-838. 1975. crossref(new window)

Babuska. R, Roubos. J. A. Verbruggen. H. B, "Identification of MIMO systems by input-output TS models". IEEE International Conference on Fuzzy Systems, Anchorage, USA, Vol. 1, pp. 657-662. 1998.

Abonyi, J and Balazs F, Cluster Analysis for Data Mining and system identification. Birkhauser Verlag AGm. Berlin 2007

Abonyi. J, Fuzzy Modeling Identification for Control. Birkhauser, Boston. 2003.

Babuska. R, Fuzzy modeling and identification toolbox. Users guide. Control Engineering Laboratory, Faculty of Information Technology and Systems, Delft University of Technology, Delft, The Netherlands, version 3.3, ed. 2002.

Balasko. B, Abonyi. J et Balazs. L, Fuzzy clustering and data analysis toolbox for use with matlab.

Gustafson. D.E., Kessel. W.C, "Fuzzy clustering with a fuzzy covariance matrix", Proceedings of the IEEE CDC, San Diego, CA, USA, pp.761-766. 1979.

R. Babuska, P.J. van der Veen, U. Kaymak, "Improved covariance estimation for Gustafson-Kessel clustering", Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, Vol. 2. pp. 1081-1085. 2002.