The Parameters Selection of PSO Algorithm influencing On performance of Fault Diagnosis

被引：54

作者：

He, Yan ^{[1
]}

Ma, Wei Jin ^{[1
]}

Zhang, Ji Ping ^{[1
]}

机构：

[1] North Univ China, Sch Mech Engn & Power Engn, Taiyuan 030051, Shanxi, Peoples R China

来源：

2016 INTERNATIONAL CONFERENCE ON MECHATRONICS, MANUFACTURING AND MATERIALS ENGINEERING (MMME 2016) | 2016年 / 63卷

关键词：

D O I：

10.1051/matecconf/20166302019

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The particle swarm optimization (PSO) is an optimization algorithm based on intelligent optimization. Parameters selection of PSO will play an important role in performance and efficiency of the algorithm. In this paper, the performance of PSO is analyzed when the control parameters vary, including particle number, accelerate constant, inertia weight and maximum limited velocity. And then PSO with dynamic parameters has been applied on the neural network training for gearbox fault diagnosis, the results with different parameters of PSO are compared and analyzed. At last some suggestions for parameters selection are proposed to improve the performance of PSO.

引用

页数：5

共 7 条

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