{"title":"基于总最小二乘的改进灰色模型gm(1,1)算法及其在变形预测中的应用","authors":"Lu Tieding, Zhou Shijian, Liu Wei, Zhang Liting","doi":"10.1109/GSIS.2009.5408291","DOIUrl":null,"url":null,"abstract":"This paper presents an improved algorithm for grey model-GM(1,1) based on total least squares(TLS). As we know that the parameters a and b in grey model-GM(1,1) can be solved by the Least squares method. The LS method is based on an assumption that vector Y contains errors while repeated additive matrix B is accurate in GM(1,1). When we analyze the element of matrix B, the matrix B also contains errors in fact. TLS is the method of fitting that is appropriate when there are errors in both vector Y and matrix B. The calculated results of an example show that the prediction model based on TLS can enhance the prediction accuracy.","PeriodicalId":294363,"journal":{"name":"2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"An improved algorithm of grey model-GM(1,1) based on total least squares and its application in deformation forecast\",\"authors\":\"Lu Tieding, Zhou Shijian, Liu Wei, Zhang Liting\",\"doi\":\"10.1109/GSIS.2009.5408291\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an improved algorithm for grey model-GM(1,1) based on total least squares(TLS). As we know that the parameters a and b in grey model-GM(1,1) can be solved by the Least squares method. The LS method is based on an assumption that vector Y contains errors while repeated additive matrix B is accurate in GM(1,1). When we analyze the element of matrix B, the matrix B also contains errors in fact. TLS is the method of fitting that is appropriate when there are errors in both vector Y and matrix B. The calculated results of an example show that the prediction model based on TLS can enhance the prediction accuracy.\",\"PeriodicalId\":294363,\"journal\":{\"name\":\"2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GSIS.2009.5408291\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GSIS.2009.5408291","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An improved algorithm of grey model-GM(1,1) based on total least squares and its application in deformation forecast
This paper presents an improved algorithm for grey model-GM(1,1) based on total least squares(TLS). As we know that the parameters a and b in grey model-GM(1,1) can be solved by the Least squares method. The LS method is based on an assumption that vector Y contains errors while repeated additive matrix B is accurate in GM(1,1). When we analyze the element of matrix B, the matrix B also contains errors in fact. TLS is the method of fitting that is appropriate when there are errors in both vector Y and matrix B. The calculated results of an example show that the prediction model based on TLS can enhance the prediction accuracy.