Tianshe Yang, Bin Chen, Hailong Zhang, Xiaole Wang, Yu Gao, Nan Xing
{"title":"State trend prediction of spacecraft based on BP neural network","authors":"Tianshe Yang, Bin Chen, Hailong Zhang, Xiaole Wang, Yu Gao, Nan Xing","doi":"10.1109/MIC.2013.6758086","DOIUrl":null,"url":null,"abstract":"According to the requirement of state trend prediction for spacecraft fault prediction, a spacecraft state trend prediction method is proposed based on BP neural network. The principle and model of BP neural network are introduced into spacecraft fault prediction. Considering the specific application background, the relevant algorithm flow is provided. Taking the temperature parameter of key components in satellite as research object, the state trend prediction computation and comparison are implemented. The precision of the prediction results is evaluated, and it verifies the reliability and validity of the proposed method in quantitative way.","PeriodicalId":404630,"journal":{"name":"Proceedings of 2013 2nd International Conference on Measurement, Information and Control","volume":"1150 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 2013 2nd International Conference on Measurement, Information and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MIC.2013.6758086","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
According to the requirement of state trend prediction for spacecraft fault prediction, a spacecraft state trend prediction method is proposed based on BP neural network. The principle and model of BP neural network are introduced into spacecraft fault prediction. Considering the specific application background, the relevant algorithm flow is provided. Taking the temperature parameter of key components in satellite as research object, the state trend prediction computation and comparison are implemented. The precision of the prediction results is evaluated, and it verifies the reliability and validity of the proposed method in quantitative way.