{"title":"Research on the reliability of automotive sensors based on minimum relative information entropy","authors":"Tao Liu, Duwei Gong","doi":"10.1109/AIAM57466.2022.00125","DOIUrl":null,"url":null,"abstract":"Based on the principle of minimum relative information entropy, a new compound assignment method is proposed to establish an automotive sensor reliability evaluation model as an example. In order to improve the ease of use, a BP network is constructed with a single indicator as the collection sample and the diagnosis result as the output sample. It is verified by MATLAB simulation that the average relative error of prediction is 0.64% and the maximum relative error is 1.61%, which indicates that the model can give evaluation results quickly within a reasonable range and has certain application value for automotive sensor reliability evaluation.","PeriodicalId":439903,"journal":{"name":"2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIAM57466.2022.00125","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Based on the principle of minimum relative information entropy, a new compound assignment method is proposed to establish an automotive sensor reliability evaluation model as an example. In order to improve the ease of use, a BP network is constructed with a single indicator as the collection sample and the diagnosis result as the output sample. It is verified by MATLAB simulation that the average relative error of prediction is 0.64% and the maximum relative error is 1.61%, which indicates that the model can give evaluation results quickly within a reasonable range and has certain application value for automotive sensor reliability evaluation.