{"title":"CARAR系统的一种新的最小二乘迭代估计算法","authors":"Lijuan Wan, Chunping Chen, Yan Ji","doi":"10.1109/DDCLS.2018.8515986","DOIUrl":null,"url":null,"abstract":"Mathematical models are the base for the system analysis and the controller design. This paper focuses on the identification problems of controlled autoregressive models with autoregressive noise (CARAR system for short). By applying the iterative method and the hierarchical principle, a least squares identification algorithm is investigated. The key of this algorithm is replacing the unknown noise terms in the information vector with their estimated residuals. The effectiveness of this approach is demonstrated by the simulation experiment.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"11 1","pages":"1170-1173"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A New Least Squares Iterative Estimation Algorithm for CARAR Systems\",\"authors\":\"Lijuan Wan, Chunping Chen, Yan Ji\",\"doi\":\"10.1109/DDCLS.2018.8515986\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mathematical models are the base for the system analysis and the controller design. This paper focuses on the identification problems of controlled autoregressive models with autoregressive noise (CARAR system for short). By applying the iterative method and the hierarchical principle, a least squares identification algorithm is investigated. The key of this algorithm is replacing the unknown noise terms in the information vector with their estimated residuals. The effectiveness of this approach is demonstrated by the simulation experiment.\",\"PeriodicalId\":6565,\"journal\":{\"name\":\"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)\",\"volume\":\"11 1\",\"pages\":\"1170-1173\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DDCLS.2018.8515986\",\"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 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DDCLS.2018.8515986","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Least Squares Iterative Estimation Algorithm for CARAR Systems
Mathematical models are the base for the system analysis and the controller design. This paper focuses on the identification problems of controlled autoregressive models with autoregressive noise (CARAR system for short). By applying the iterative method and the hierarchical principle, a least squares identification algorithm is investigated. The key of this algorithm is replacing the unknown noise terms in the information vector with their estimated residuals. The effectiveness of this approach is demonstrated by the simulation experiment.
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