@inproceedings{5a27d46502274e53992002e0e2c98662,
title = "Study on method of modelling and controlling of magnetostrictive material",
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.",
author = "An Jinlong and Yang Qingxin and Mazhengpin",
year = "2006",
language = "英语",
isbn = "3952299049",
series = "17th International Zurich Symposium on Electromagnetic Compatibility, 2006",
pages = "387--390",
booktitle = "17th International Zurich Symposium on Electromagnetic Compatibility, 2006",
note = "17th International Zurich Symposium on Electromagnetic Compatibility, 2006 ; Conference date: 27-02-2006 Through 03-03-2006",
}