Measurement Error Compensation Method for Parameters of Rear Torsion Beam With PSO-BP

Kangkang Zhang, Bo Liu
{"title":"Measurement Error Compensation Method for Parameters of Rear Torsion Beam With PSO-BP","authors":"Kangkang Zhang, Bo Liu","doi":"10.1109/SDPC.2019.00032","DOIUrl":null,"url":null,"abstract":"In the process of inspecting the rear torsion beam, there will be measurement error because of the manufacturing error, vibration of the automatic inspection tool and the deformation of the workpiece. This paper presents an error compensation method for parameter of rear torsion beam based on PSO-BP (particle swarm optimization and back propagation neural network) algorithm. In order to solve the problem that BP neural network converges slowly and is easy to fall into local optimum, the paper uses PSO algorithm to optimize its weight and threshold. The research results show that the PSO-BP algorithm has good error compensation accuracy.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SDPC.2019.00032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In the process of inspecting the rear torsion beam, there will be measurement error because of the manufacturing error, vibration of the automatic inspection tool and the deformation of the workpiece. This paper presents an error compensation method for parameter of rear torsion beam based on PSO-BP (particle swarm optimization and back propagation neural network) algorithm. In order to solve the problem that BP neural network converges slowly and is easy to fall into local optimum, the paper uses PSO algorithm to optimize its weight and threshold. The research results show that the PSO-BP algorithm has good error compensation accuracy.
基于PSO-BP的后扭力梁参数测量误差补偿方法
在对后扭力梁进行检测的过程中,由于制造误差、自动检测工具的振动以及工件的变形等原因,会产生测量误差。提出了一种基于PSO-BP (particle swarm optimization and back propagation neural network)算法的后扭力梁参数误差补偿方法。为了解决BP神经网络收敛速度慢、容易陷入局部最优的问题,本文采用粒子群算法对BP神经网络的权值和阈值进行优化。研究结果表明,PSO-BP算法具有良好的误差补偿精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信