NDLI: Hysteresis modeling with least squares support vector machines

Content Provider	IEEE Xplore Digital Library
Author	Kang Chuanhui Wang Xiaodong Wang Ke Chang Jianli
Copyright Year	2010
Description	Author affiliation: Department of Electronic Engineering, Zhejiang Normal University, Jinhua 321004, China (Kang Chuanhui; Wang Xiaodong; Wang Ke; Chang Jianli)
Abstract	The least squares support vector machines (LS-SVMs) are proposed for hysteresis system modeling in this investigation. The historical information of hysteresis system is used as a part of the input signals of the LS-SVMs so that the many-to-one mapping of hysteresis system is transformed into a one-to-one mapping. Utilizing the nonlinear approximation ability of LS-SVMs, a dynamic hysteresis model can be obtained by the proposed method. A soft magnetic material has been used as an example for demonstrating the effectiveness of the LS-SVM method. As a comparison, the popular neural networks (NNs) are also applied to the hysteresis system modeling. The experimental results show that the proposed method has an advantage over NNs in modeling accuracy and training time. In terms of the demonstration of soft magnetic material, the LS-SVMs have one order of magnitude lower than the NNs in modeling error. Also, the training time of LS-SVMs is only one-third of the NNs. These mean that the LS-SVM method overcomes some shortcomings of NN method in hysteresis system modeling.
Starting Page	1330
Ending Page	1333
File Size	331698
Page Count	4
File Format	PDF
ISBN	9781424462636
e-ISBN	9787894631046
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Association of Aut
Subject Keyword	Support vector machines Artificial neural networks Neural Networks Magnetic hysteresis Electronic mail Least Squares Support Vector Machines Mathematical model Soft magnetic materials Modeling Hysteresis
Content Type	Text
Resource Type	Article

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