A Fault Diagnosis Algorithm of Artificial Immune Network Model based on Neighborhood Rough Set Theory

被引：0

作者：

Zheng, Yonghuang ^{[1
]}

Tian, Feng ^{[1
]}

Li, Renhou ^{[1
]}

Song, Qingsong ^{[1
]}

Li, Longzhuang ^{[2
]}

机构：

[1] Xi An Jiao Tong Univ, SKLMS Lab, Xian 710049, Peoples R China

[2] Texas A&M Univ Corpus Christi, Sch Engn & Comp Sci, Corpus Christi, TX USA

来源：

2012 6TH INTERNATIONAL CONFERENCE ON NEW TRENDS IN INFORMATION SCIENCE, SERVICE SCIENCE AND DATA MINING (ISSDM2012) | 2012年

基金：

美国国家科学基金会;

关键词：

Neighborhood rough set; artificial immune network; fault diagnosis; the pruning threshold adjustment; SYSTEM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a fault diagnosis algorithm of artificial immune network model based on neighborhood rough set theory. In the algorithm, the relationships between pruning threshold, the rates of mis-diagnosis, and missed diagnosis are discussed in the shape space. In addition, the fault mode boundaries, the fault mode inclusion relations, an observation index and an algorithm for adaptively adjusting pruning threshold are described. The simulation experiments show that the proposed fault diagnosis algorithm can identify the unknown and untrained fault modes, while keeping misdiagnosis rate and missed diagnosis rate low.

引用

页码：621 / 627

页数：7

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