{"title":"基于EM的一般线性状态空间系统估计","authors":"Tong Zhang","doi":"10.1109/CCDC.2018.8407657","DOIUrl":null,"url":null,"abstract":"EM-KF algorithm is very widely used, such as blind source separation and so on. However, due to the lack of research on the more general state space model with input data, it is difficult to apply to the real industrial system. In this paper, the EM-KF algorithm is successfully improved for the more general state space model. A simulation example is given to demonstrate the effectiveness of the algorithm.","PeriodicalId":409960,"journal":{"name":"2018 Chinese Control And Decision Conference (CCDC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"EM based estimation for general linear state space systems\",\"authors\":\"Tong Zhang\",\"doi\":\"10.1109/CCDC.2018.8407657\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"EM-KF algorithm is very widely used, such as blind source separation and so on. However, due to the lack of research on the more general state space model with input data, it is difficult to apply to the real industrial system. In this paper, the EM-KF algorithm is successfully improved for the more general state space model. A simulation example is given to demonstrate the effectiveness of the algorithm.\",\"PeriodicalId\":409960,\"journal\":{\"name\":\"2018 Chinese Control And Decision Conference (CCDC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Chinese Control And Decision Conference (CCDC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCDC.2018.8407657\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Chinese Control And Decision Conference (CCDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCDC.2018.8407657","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
EM based estimation for general linear state space systems
EM-KF algorithm is very widely used, such as blind source separation and so on. However, due to the lack of research on the more general state space model with input data, it is difficult to apply to the real industrial system. In this paper, the EM-KF algorithm is successfully improved for the more general state space model. A simulation example is given to demonstrate the effectiveness of the algorithm.
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