location: Current position: Home >> Scientific Research >> Paper Publications

Learning solutions to two dimensional electromagnetic equations using LS-SVM

Hits:

Indexed by:期刊论文

First Author:Han, Xiaoming

Correspondence Author: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.

Co-author:Wang, Jinjun,Wu, Ziku,Li, Guofeng,Wu, Yan,Li, Juan

Date of Publication:2018-11-23

Journal:NEUROCOMPUTING

Included Journals:SCIE、Scopus

Volume:317

Page Number:15-27

ISSN No.:0925-2312

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

Abstract: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.

Pre One:Excellent energy storage performance and thermal property of polymer-based composite induced by multifunctional one-dimensional nanofibers oriented in-plane direction

Next One:Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network