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    李国锋

    • 教授     博士生导师   硕士生导师
    • 性别:男
    • 毕业院校:大连理工大学
    • 学位:博士
    • 所在单位:电气工程学院
    • 学科:电工理论与新技术
    • 办公地点:A3区32号楼静电与特种电源研究所201室
    • 联系方式:+86-411-84706489(O)
    • 电子邮箱:guofenli@dlut.edu.cn

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    Learning solutions to two dimensional electromagnetic equations using LS-SVM

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    论文类型:期刊论文

    第一作者:Han, Xiaoming

    通讯作者:Han, XM (reprint author), Dalian Univ Technol, Sch Elect Engn, Dalian 116024, Peoples R China.; Wu, ZK (reprint author), Qingdao Agr Univ, Sci & Informat Coll, Qingdao 266109, Peoples R China.

    合写作者:Wang, Jinjun,Wu, Ziku,Li, Guofeng,Wu, Yan,Li, Juan

    发表时间:2018-11-23

    发表刊物:NEUROCOMPUTING

    收录刊物:SCIE、Scopus

    卷号:317

    页面范围:15-27

    ISSN号:0925-2312

    关键字:Linear electromagnetic equation; Nonlinear electromagnetic equation; Multimedia electromagnetic equation; Discontinuous boundary conditions; Least squares support vector machines; Cubic spline

    摘要:In this paper, a new approach based on least squares support vector machines (LS-SVM) is proposed for solving the electromagnetic equations. Firstly, the cubic spline function is employed to smooth the discontinuous boundary. LS-SVM is used to solve the modified problem. Secondly, nonlinear electromagnetic equation is solved by LS-SVM. Finally, multimedia electromagnetic equation is solved by LS-SVM. Same as to the artificial neural networks (ANN), the approximate solutions are composed of two parts. The first part is a known function that satisfies the boundary conditions. The second part is the product of two terms. One term is also a known function which vanished on the boundary. The left part is the combination of kernel functions containing regression parameters. The parameters can be obtained by solving a system of equations. The numerical results show that the proposed method in this paper is feasible. (C) 2018 Elsevier B.V. All rights reserved.