{"title":"变形监测数据的RBF神经网络预测方法","authors":"Guo-hui Wang, Ma Li, Hai-tao Chen","doi":"10.1109/MACE.2010.5536200","DOIUrl":null,"url":null,"abstract":"In order to improve the precision and reliability of prediction of deformation monitoring data, radial basis function artificial neural network is used in deformation monitoring data processing. The prediction result of this method is compared with the prediction result of BP neural network prediction methods, and it is concluded that through the radial basis function artificial neural network better prediction result can be obtained.","PeriodicalId":6349,"journal":{"name":"2010 International Conference on Mechanic Automation and Control Engineering","volume":"23 1","pages":"4874-4876"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"RBF neural network prediction method of deformation monitoring data\",\"authors\":\"Guo-hui Wang, Ma Li, Hai-tao Chen\",\"doi\":\"10.1109/MACE.2010.5536200\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to improve the precision and reliability of prediction of deformation monitoring data, radial basis function artificial neural network is used in deformation monitoring data processing. The prediction result of this method is compared with the prediction result of BP neural network prediction methods, and it is concluded that through the radial basis function artificial neural network better prediction result can be obtained.\",\"PeriodicalId\":6349,\"journal\":{\"name\":\"2010 International Conference on Mechanic Automation and Control Engineering\",\"volume\":\"23 1\",\"pages\":\"4874-4876\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Mechanic Automation and Control Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MACE.2010.5536200\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Mechanic Automation and Control Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MACE.2010.5536200","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
RBF neural network prediction method of deformation monitoring data
In order to improve the precision and reliability of prediction of deformation monitoring data, radial basis function artificial neural network is used in deformation monitoring data processing. The prediction result of this method is compared with the prediction result of BP neural network prediction methods, and it is concluded that through the radial basis function artificial neural network better prediction result can be obtained.