{"title":"工艺数据中粗差检测方法的比较","authors":"D. Maquin, J. Ragot","doi":"10.1109/CDC.1991.261549","DOIUrl":null,"url":null,"abstract":"The authors first discuss the fundamental problem of data reconciliation. They then prove the equivalence of some tests commonly used for gross error detection: parity vector, normalized corrective terms, the generalized likelihood ratio test, and variation of the residual criterion after measurement deletion.<<ETX>>","PeriodicalId":344553,"journal":{"name":"[1991] Proceedings of the 30th IEEE Conference on Decision and Control","volume":"263 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Comparison of gross errors detection methods in process data\",\"authors\":\"D. Maquin, J. Ragot\",\"doi\":\"10.1109/CDC.1991.261549\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors first discuss the fundamental problem of data reconciliation. They then prove the equivalence of some tests commonly used for gross error detection: parity vector, normalized corrective terms, the generalized likelihood ratio test, and variation of the residual criterion after measurement deletion.<<ETX>>\",\"PeriodicalId\":344553,\"journal\":{\"name\":\"[1991] Proceedings of the 30th IEEE Conference on Decision and Control\",\"volume\":\"263 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1991] Proceedings of the 30th IEEE Conference on Decision and Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.1991.261549\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1991] Proceedings of the 30th IEEE Conference on Decision and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.1991.261549","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of gross errors detection methods in process data
The authors first discuss the fundamental problem of data reconciliation. They then prove the equivalence of some tests commonly used for gross error detection: parity vector, normalized corrective terms, the generalized likelihood ratio test, and variation of the residual criterion after measurement deletion.<>