DocumentCode :
477764
Title :
Data Mining Modeling for Electromagnetic Scattering Computing
Author :
Chen, Weishi ; Ning, Huansheng ; Mao, Xia ; Wang, Baofa
Author_Institution :
Sch. of Electron. & Inf. Eng., Beihang Univ., Beijing
Volume :
2
fYear :
2008
fDate :
18-20 Oct. 2008
Firstpage :
137
Lastpage :
140
Abstract :
Scattering computing of metal-media complex structure and structure with cavity has been one of the problems in electromagnetic (EM) scattering theoretical calculation field for many years. A novel method, substituting data mining modeling for original theoretical modeling, is proposed creatively in this paper, attempting to solve the problem by machine learning theory. Data mining modeling is to construct "EM scattering training model", applying regression analysis algorithm on measurement data, to achieve the effect superior to that theoretical modeling can have. Given an example of regressive estimation of some inlet backscattering RCS curve, both original least square algorithm and support vector regression are used, so an applicable data mining model is established initially for EM scattering computing.
Keywords :
data mining; electromagnetic wave propagation; learning (artificial intelligence); least squares approximations; regression analysis; support vector machines; data mining modeling; electromagnetic scattering computing; inlet backscattering RCS curve; least square algorithm; machine learning; metal-media complex structure; regression analysis algorithm; regressive estimation; support vector regression; Data mining; Electromagnetic modeling; Electromagnetic scattering; Machine learning; Machine learning algorithms; Radar cross section; Radar detection; Radar scattering; Radar tracking; Regression analysis; RCS; data mining; least square; support vector regression;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on
Conference_Location :
Shandong
Print_ISBN :
978-0-7695-3305-6
Type :
conf
DOI :
10.1109/FSKD.2008.578
Filename :
4666095
Link To Document :
بازگشت