An Improved PSO Algorithm and Its Application on Fault Diagnosis

被引：0
|
作者：
Liu, X. L. [1 ]
Cao, L. H. [1 ]
Wang, S. T. [1 ]
Li, J. N. [1 ]
Huang, Y. [1 ]
Li, Y. P. [2 ]
机构：
[1] Chongqing Commun Inst, Chongqing, Peoples R China
[2] PLA77556 Troop, Tibet, Peoples R China
来源：
PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INDUSTRIAL ENGINEERING (AIIE 2015) | 2015年 / 123卷
关键词：
particle swarm optimization; multi-population; support vector machine; fault diagnosis; OPTIMIZATION;
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：
For the disadvantages of PSO (Particle Swarm Optimization) algorithm, such as premature convergence and easily getting into local extremum, an improved PSO algorithm was presented in this paper. On the one hand, the population with worse performance moved near the global optimization value of the other population with certain probability; on the other hand, one population was randomly chosen to mutate to stimulate the particles jump out the local extremum when the two populations continuously trapped into the same local extremum. The simulation results showed that the improved PSO had a better optimization performance. SVM (Support Vector Machine) trained the improved PSO was applied to fault diagnosis of diesel engine valve. The simulation results showed that the improved PSO-SVM acquired higher accuracy.
引用
收藏
页码：353 / 356
页数：4
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