Application of Multi-Sensor Information Fusion Method Based on Rough Sets and Support Vector Machine

被引：0

作者：

Xue, Jinxue ^{[1
]}

Wang, Guohu ^{[1
]}

Wang, Xiaoqiang ^{[1
]}

Cui, Fengkui ^{[1
]}

机构：

[1] Henan Univ Sci & Technol, Mech & Elect Engn Coll, Luoyang 471003, Peoples R China

来源：

PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON MECHANICAL ENGINEERING AND CONTROL SYSTEMS (MECS2015) | 2016年

关键词：

information fusion; rough set theory; Support Vector Machine; attribute reduction;

D O I：

暂无

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 order to improve the precision and date processing speed of multi-sensor information fusion, a kind of multi-sensor data fusion process algorithm has been studied in this paper. First, based on rough set theory (RS) to attribute reduction the parameter set, we use the advantages of rough set theory in dealing with large amount of data to eliminate redundant information. Then, the data can be trained and classified by Support Vector Machine (SVM). Experimental results showed that this method can improve the speed and accuracy of multi-sensor fusion system.

引用

页码：350 / 353

页数：4

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