A modified PSO-BP algorithm in hydraulic system fault diagnosis application

摘要：

　　BP neural network for failure pattern recognition has been used in hydraulic system fault diagnosis.However,its convergence rate is relatively small and always trapped at the local minima.So a new modified PSO-BP hydraulic system fault diagnosis method was proposed,which combined the respective advantages of particle swarm algorithm and BP algorithm.Firstly,the inertia weight and learning factor of the standard particle swarm algorithm was improved,then BP neural networks weights and thresholds were optimized by modified PSO algorithm.BP network performance was ameliorated.The simulation results showed that this method improved the convergence rate of the BP network,and it could reduce the diagnostic errors.

关键词： PSO algorithm BP neural network hydraulic system fault diagnosis

作者: Zengshou Dong Xiaoyu Zhang Jianchao Zeng

作者单位: Dept.of Electronic Information Engineering,Taiyuan University of Science and Technology,Taiyuan,P.R. The System Simulation and Computer Application Research Laboratory,Taiyuan University of Science and

会议类型: 国际会议

会议名称: 2012 International Applied Mechanics,Mechatronics Automation Symposium(2012应用力学,机电一体化自动化国际研讨会)(IAMMAS2012)

会议地点: 沈阳

会议语种:英文

页码: 1145-1148

在线出版日期: 2012-09-07（万方平台首次上网日期，不代表论文的发表时间）