Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine

被引：92

作者：

Zhao Chenglin ^{[1
]}

Sun Xuebin ^{[1
]}

Sun Songlin ^{[2
]}

Jiang Ting ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Key Lab Universal Wireless Commun, Minist Educ, Beijing 100876, Peoples R China

[2] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2011年 / 38卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Chaos particle swarm optimization algorithm; Wireless sensor; Fault diagnosis; Support vector machine; Chaos queues;

D O I：

10.1016/j.eswa.2011.02.043

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

Fault diagnosis of sensor timely and accurately is very important to improve the reliable operation of systems. In the study, fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine is presented in the paper, where chaos particle swarm optimization is chosen to determine the parameters of SVM. Chaos particle swarm optimization is a kind of improved particle swarm optimization, which can not only avoid the search being trapped in local optimum and but also help to search the optimum quickly by using chaos queues. The wireless sensor is employed as research object, and its four fault types including shock, biasing, short circuit and shifting are applied to test the diagnostic ability of CPSO-SVM compared with other diagnostic methods. The diagnostic results show that CPSO-SVM has higher diagnostic accuracy of wireless sensor than PSO-SVM and BP neural network. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：9908 / 9912

页数：5

共 11 条

[1]

Chaos embedded particle swarm optimization algorithms [J].