A Support Vector Machine for Regression in Complex Field

被引：0
|
作者：
Lang, Rongling [1 ]
Zhao, Fei [1 ]
Shi, Yongtang [2 ,3 ]
机构：
[1] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China
[2] Nankai Univ, Ctr Combinator, Tianjin, Peoples R China
[3] Nankai Univ, LPMC, Tianjin, Peoples R China
来源：
INFORMATICA | 2017年 / 28卷 / 04期
基金：
中国国家自然科学基金;
关键词：
support vector machine for regression; complex field; kernel function; ADAPTIVE ARRAY;
D O I：
10.15388/Informatica.2017.150
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, one method for training the Support Vector Regression (SVR) machine in the complex data field is presented, which takes into account all the information of both the real and imaginary parts simultaneously. Comparing to the existing methods, it not only considers the geometric information of the complex-valued data, but also can be trained with the same amount of computation as the original SVR in the real data field. The accuracy of the proposed method is analysed by the simulation experiments. This also can be applied to the field of anti-interference for satellite navigation successfully, which shows its effectiveness in practical application.
引用
收藏
页码：651 / 664
页数：14
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