NDLI: Study on method of modelling and controlling of magnetostrictive material

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Study on method of modelling and controlling of magnetostrictive material

Content Provider	IEEE Xplore Digital Library
Author	Jinlong An Qingxin Yang Mazhengpin
Copyright Year	2006
Description	Author affiliation: Key Lab. of Electromagn. Field & Electr. Apparautus Reliability, Hebei Univ. of Technol. (Jinlong An; Qingxin Yang; Mazhengpin)
Abstract	Support vector machine is a learning technique based on the structural risk minimization principle, and it is also a kind of regression method with good generalization ability. This paper analyses the disadvantage of the nonlinear dynamical systems identification method based on neural networks, and presents a SVM method of modelling and controlling for magnetostrictive material. Simulation result indicates that this method has the better prediction precision than that of the approach based on the neural network. Therefore the present method can be used to the prediction control of magnetostrictive material
Starting Page	387
Ending Page	390
File Size	275825
Page Count	4
File Format	PDF
ISBN	3952299030
DOI	10.1109/EMCZUR.2006.214952
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-02-27
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	ETH Zurich, Swiss Federal Institute
Subject Keyword	Support vector machines Magnetostriction Neural networks Machine learning Nonlinear control systems Predictive models Magnetic materials Magnetic analysis Nonlinear dynamical systems Risk management
Content Type	Text
Resource Type	Article

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