This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery
Data Mining Modeling for Electromagnetic Scattering Computing
October 18-October 20
ISBN: 978-0-7695-3305-6
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.
Index Terms:
data mining, least square, support vector regression, RCS
Citation:
Weishi Chen, Huansheng Ning, Xia Mao, Baofa Wang, "Data Mining Modeling for Electromagnetic Scattering Computing," fskd, vol. 2, pp.137-140, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008
Usage of this product signifies your acceptance of the Terms of Use.