{"title":"连续时间分段自适应状态估计的收敛性","authors":"Jitendra Tugnait","doi":"10.1109/CDC.1980.271884","DOIUrl":null,"url":null,"abstract":"The asymptotic behavior of Bayes optimal adaptive state estimation schemes (also called the partitioned adaptive estimation algorithms) for continuous-time linear dynamic Gauss-Markov systems with unknown parameters is investigated. The unknown system parameters are assumed to belong to a finite set. The results are developed through weak consistency of the maximum likelihood and the maximum a posteriori probability estimates of the unknown parameters.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1980-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Convergence of continuous-time partitioned adaptive state estimators\",\"authors\":\"Jitendra Tugnait\",\"doi\":\"10.1109/CDC.1980.271884\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The asymptotic behavior of Bayes optimal adaptive state estimation schemes (also called the partitioned adaptive estimation algorithms) for continuous-time linear dynamic Gauss-Markov systems with unknown parameters is investigated. The unknown system parameters are assumed to belong to a finite set. The results are developed through weak consistency of the maximum likelihood and the maximum a posteriori probability estimates of the unknown parameters.\",\"PeriodicalId\":332964,\"journal\":{\"name\":\"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1980-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.1980.271884\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.1980.271884","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Convergence of continuous-time partitioned adaptive state estimators
The asymptotic behavior of Bayes optimal adaptive state estimation schemes (also called the partitioned adaptive estimation algorithms) for continuous-time linear dynamic Gauss-Markov systems with unknown parameters is investigated. The unknown system parameters are assumed to belong to a finite set. The results are developed through weak consistency of the maximum likelihood and the maximum a posteriori probability estimates of the unknown parameters.
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