FUZZY SENSOR FUSION BASED ON EVIDENCE THEORY AND ITS APPLICATION

被引：34

作者：

Chen, Shiyu ^{[1
]}

Deng, Yong ^{[1
,2
]}

Wu, Jiyi ^{[2
]}

机构：

[1] Southwest Univ, Sch Comp & Informat Sci, Chongqing 400715, Peoples R China

[2] Hangzhou Normal Univ, Hangzhou Key Lab E Business & Informat Secur, Hangzhou, Zhejiang, Peoples R China

来源：

APPLIED ARTIFICIAL INTELLIGENCE | 2013年 / 27卷 / 03期

基金：

国家高技术研究发展计划(863计划); 中国国家自然科学基金;

关键词：

RELIABILITY; DISTANCE; SYSTEM; LOGIC; COST;

D O I：

10.1080/08839514.2013.769078

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In multisensor systems, complementary observations from different sensors need to be combined with each other. Due to the uncertainty, sensor reports can be represented by fuzzy sets in order to efficiently deal with signal processing. In this article, a methodology to combine sensor reports in fuzzy environments based on DempsterShafer evidence theory is proposed. The basic probability assignment function is constructed by means of member functions. The numerical example on object recognition of a robot arm is shown to illustrate the efficiency of the presented approach.

引用

页码：235 / 248

页数：14

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