{"title":"Identification of dynamic systems from noisy data: the case m*=n-1","authors":"B. Anderson, M. Deistler","doi":"10.1109/CDC.1991.261692","DOIUrl":null,"url":null,"abstract":"Linear dynamic errors-in-variables (or factor) models in the framework of stationary processes are considered. The noise process is assumed to have a diagonal spectral density. The relation between the (population) second moments of the observations and the system and noise characteristics is analyzed; of particular interest are the number of equations (or the number of factors) and a description of the set of all systems compatible with the second moments of the observations. Emphasis is placed on the case which can be reduced to a single factor. The problems considered arise in the context of identification and precede estimation.<<ETX>>","PeriodicalId":344553,"journal":{"name":"[1991] Proceedings of the 30th IEEE Conference on Decision and Control","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","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.1991.261692","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Linear dynamic errors-in-variables (or factor) models in the framework of stationary processes are considered. The noise process is assumed to have a diagonal spectral density. The relation between the (population) second moments of the observations and the system and noise characteristics is analyzed; of particular interest are the number of equations (or the number of factors) and a description of the set of all systems compatible with the second moments of the observations. Emphasis is placed on the case which can be reduced to a single factor. The problems considered arise in the context of identification and precede estimation.<>
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