{"title":"基于PSO-BP的后扭力梁参数测量误差补偿方法","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":"{\"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}","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
摘要
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。
Measurement Error Compensation Method for Parameters of Rear Torsion Beam With PSO-BP
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.