MATEC Web Conf.
Volume 63, 20162016 International Conference on Mechatronics, Manufacturing and Materials Engineering (MMME 2016)
|Number of page(s)||5|
|Section||Manufacturing and Design Science|
|Published online||12 July 2016|
The Parameters Selection of PSO Algorithm influencing On performance of Fault Diagnosis
School of Mechanical Engineering and Power Engineer North University of China, Taiyuan, Shanxi 030051, China
a Yan HE: email@example.com
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.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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